[{"data":1,"prerenderedAt":205},["ShallowReactive",2],{"DlFXI4Eibt_Bn9lrEZz1TYbHCWFZj3IvqwHQSEW-Exc":3,"kzuvooxGQNAzOXL-m7nTKHQAswMfc8FZqG1dhGqPekQ":194},{"code":4,"msg":5,"data":6},0,"",{"category":7,"tag":11,"hot":39,"new":78,"banner":118,"data":143,"cache":193},[8,9,10],"Agent","OpenAI","LLM",[12,14,17,20,23,25,27,30,33,36],{"title":8,"total":13},39,{"title":15,"total":16},"Google",44,{"title":18,"total":19},"Nvidia",13,{"title":21,"total":22},"Claude",11,{"title":9,"total":24},35,{"title":10,"total":26},85,{"title":28,"total":29},"DeepSeek",9,{"title":31,"total":32},"OCR",1,{"title":34,"total":35},"Chat",7,{"title":37,"total":38},"Generator",116,[40,48,55,64,71],{"id":41,"publish_date":42,"is_original":4,"collection":5,"cover_url":43,"cover_url_1_1":44,"title":45,"summary":46,"author":47},557,"2022-04-29","article_res/cover/7a9b1375ed9bb298154981bae42b794d.jpeg","article_res/cover/afa281dd52bc0454e6735daa8e6b0706.jpeg","Translation and summary of Messari Report [2.8 Kristin Smith, Blockchain Association and Katie Haun, a16z]","We need unity and speed right now.","Translation",{"id":49,"publish_date":50,"is_original":4,"collection":5,"cover_url":51,"cover_url_1_1":52,"title":53,"summary":54,"author":47},531,"2022-05-25","article_res/cover/e8362057f8fa189594c60afdfaaeb6e5.jpeg","article_res/cover/8ea08d0d6fa7eee6b57ed4ec61b61ad6.jpeg","Decentralized Society: Finding Web3’s Soul / Decentralized Society: Finding the Soul of Web3 -7","Decentralization through Pluralism When analyzing ecosystems, it's desirable to measure how decentralized it is.",{"id":56,"publish_date":57,"is_original":32,"collection":58,"cover_url":59,"cover_url_1_1":60,"title":61,"summary":62,"author":63},127,"2024-11-14","#Google #AI Game #World Model #AI Story","article_res/cover/0233a875b7ec2debf59779e311547569.jpeg","article_res/cover/6ffddb6ae4914b3c699493311aa9f198.jpeg","Google Launches \"Unbounded\": A Generative Infinite Character Life Simulation Game","Unbounded: A Generative Infinite Game of Character Life Simulation","Renee's Entrepreneurial Journey",{"id":13,"publish_date":65,"is_original":32,"collection":66,"cover_url":67,"cover_url_1_1":68,"title":69,"summary":70,"author":63},"2025-02-14","#Deep Dive into LLMs #Andrej Karpathy #LLM #Tool Use #Hallucination","article_res/cover/11e858ad6b74dfa80f923d549b62855c.jpeg","article_res/cover/615e1b320f1fc163edc1d2d154a6de33.jpeg","Andrej Karpathy's in-depth explanation of LLM (Part 4): Hallucinations","hallucinations, tool use, knowledge/working memory",{"id":72,"publish_date":73,"is_original":4,"collection":5,"cover_url":74,"cover_url_1_1":75,"title":76,"summary":77,"author":47},579,"2022-04-07","article_res/cover/39387376ba28447af1eb40576b9df215.jpeg","article_res/cover/02727ede8551ed49901d0abe6d6305b7.jpeg","Messari Report Translation and Summary 【1-7 Surviving the Winter】","I’d be more cautious here: 10 year and 10 hour thinking only.",[79,87,95,103,111],{"id":80,"publish_date":81,"is_original":32,"collection":82,"cover_url":83,"cover_url_1_1":84,"title":85,"summary":86,"author":63},627,"2025-03-20","#AI Avatar #AI Video Generation","article_res/cover/d95481358f73924989f8c4ee9c75d1c8.jpeg","article_res/cover/b74bc0fab01f8b6a6aa87696c0c3ed8b.jpeg","DisPose: Generating Animated Videos by Driving Video with Reference Images","DisPose is a controllable human image animation method that enhances video generation.",{"id":88,"publish_date":89,"is_original":32,"collection":90,"cover_url":91,"cover_url_1_1":92,"title":93,"summary":94,"author":63},626,"2025-03-21","#Deep Dive into LLMs #LLM #RL #Andrej Karpathy #AlphaGo","article_res/cover/446553a5c8f8f2f07d97b20eaee84e56.jpeg","article_res/cover/e6c2823409c9b34624064b9acbaca6f1.jpeg","AlphaGo and the Power of Reinforcement Learning - Andrej Karpathy's Deep Dive on LLMs (Part 9)","Simply learning from humans will never surpass human capabilities.",{"id":96,"publish_date":97,"is_original":32,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":63},625,"2025-03-22","#Deep Dive into LLMs #LLM #RL #RLHF #Andrej Karpathy","article_res/cover/8da81d38b1e5cf558a164710fd8a5389.jpeg","article_res/cover/96f028d76c362a99a0dd56389e8f7a9b.jpeg","Reinforcement Learning from Human Feedback (RLHF) - Andrej Karpathy's Deep Dive on LLMs (Part 10)","Fine-Tuning Language Models from Human Preferences",{"id":104,"publish_date":105,"is_original":32,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":63},624,"2025-03-23","#Deep Dive into LLMs #LLM #Andrej Karpathy #AI Agent #MMM","article_res/cover/a5e7c3d48bb09109684d6513287c661d.jpeg","article_res/cover/d3f22b7c0ab8d82fd2da457a299e0773.jpeg","The Future of Large Language Models - Andrej Karpathy's In-Depth Explanation of LLM (Part 11)","preview of things to come",{"id":112,"publish_date":105,"is_original":32,"collection":113,"cover_url":114,"cover_url_1_1":115,"title":116,"summary":117,"author":63},623,"#Google #Voe #AI Video Generation","article_res/cover/c44062fea0f336c2b96b3928292392c2.jpeg","article_res/cover/a041041c69092ad3db191c5bf3ff981b.jpeg","Trial of Google's video generation model VOE2","Our state-of-the-art video generation model",[119,127,135],{"id":120,"publish_date":121,"is_original":32,"collection":122,"cover_url":123,"cover_url_1_1":124,"title":125,"summary":126,"author":63},160,"2024-10-04","#Philosophy","article_res/cover/496990c49211e8b7f996b7d39c18168e.jpeg","article_res/cover/14dbaa1ade9cb4316d5829423a900362.jpeg","Time","The fungus of the morning does not know the waxing and waning of the moon, and the cicada does not know the seasons; this is a short life. To the south of the state of Chu there is a dark spirit which regards five hundred years as spring and five hundred years as autumn. In ancient times there was a great tree called the Ming which regarded eight thousand years as spring and eight thousand years as autumn; this is a long life.",{"id":128,"publish_date":129,"is_original":32,"collection":130,"cover_url":131,"cover_url_1_1":132,"title":133,"summary":134,"author":63},98,"2024-12-17","#AI Video Generator #Sora #Pika","article_res/cover/3b86e85d03fff4f356a3e4cf2bb329c9.jpeg","article_res/cover/5fa5c20ad0b40f8f544d257c0ef02938.jpeg","Pika 2.0 video generation officially released: effect comparison with Sora","今天，我们推出了Pika 2.0模型。卓越的文字对齐效果。惊人的视觉表现。还有✨场景成分✨",{"id":136,"publish_date":137,"is_original":32,"collection":138,"cover_url":139,"cover_url_1_1":140,"title":141,"summary":142,"author":63},71,"2025-01-14","#Nvidia #World Foundation Model #Cosmos #Physical AI #Embodied AI","article_res/cover/feddf8c832dfb45d28804291f6a42a9e.jpeg","article_res/cover/d6bc2f1186d96b78228c2283a17a3645.jpeg","NVIDIA's Cosmos World Model","Cosmos World Foundation Model Platform for Physical AI",[144,163,188],{"title":8,"items":145},[146,147,155],{"id":104,"publish_date":105,"is_original":32,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":63},{"id":148,"publish_date":149,"is_original":32,"collection":150,"cover_url":151,"cover_url_1_1":152,"title":153,"summary":154,"author":63},622,"2025-03-24","#OWL #AI Agent #MAS #MCP #CUA","article_res/cover/cb50ca7f2bf4d1ed50202d7406e1c19a.jpeg","article_res/cover/4aa7aa3badfacf3cc84121334f1050dd.jpeg","OWL: Multi-agent collaboration","OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation",{"id":156,"publish_date":157,"is_original":32,"collection":158,"cover_url":159,"cover_url_1_1":160,"title":161,"summary":162,"author":63},620,"2025-03-26","#LLM #Google #Gemini #AI Agent","article_res/cover/53751a6dbbe990b1eb0b63f3b062aed4.jpeg","article_res/cover/031344981f0a212ff82d1f3a64aa5756.jpeg","Gemini 2.5 Pro, claimed to be far ahead of the competition, has been released with great fanfare: comprehensively surpassing other LLMs and topping the global rankings","Gemini 2.5: Our most intelligent AI model",{"title":9,"items":164},[165,172,180],{"id":166,"publish_date":157,"is_original":32,"collection":167,"cover_url":168,"cover_url_1_1":169,"title":170,"summary":171,"author":63},619,"#OpenAI #AI Image Generator #4o #MMM #AR Transformer","article_res/cover/2faffc97fcecf3151552cb0fd3206d89.jpeg","article_res/cover/1133cb4948af44cee2e7fbe79efb69e5.jpeg","The native image function of GPT-4o is officially launched","Introducing 4o Image Generation",{"id":173,"publish_date":174,"is_original":4,"collection":175,"cover_url":176,"cover_url_1_1":177,"title":178,"summary":179,"author":63},434,"2023-07-15","#Anthropic #OpenAI #Google #AI Code Generator #Claude","article_res/cover/e1b6f600a2b9f262a4392684e5f2ce25.jpeg","article_res/cover/6e1772e83f78f9a351ab23d3e414adee.jpeg","Latest Updates on Google Bard /Anthropic Claude2 / ChatGPT Code Interpreter","We want our models to use their programming skills to provide more natural interfaces to the basic functions of our computers.  \n - OpenAI",{"id":181,"publish_date":182,"is_original":4,"collection":183,"cover_url":184,"cover_url_1_1":185,"title":186,"summary":187,"author":63},417,"2023-08-24","#OpenAI","article_res/cover/bccf897d50a88b18364e35f7466387e0.jpeg","article_res/cover/2f871085c1073717c1703ae86e18056f.jpeg","The GPT-3.5 Turbo fine-tuning (fine-tuning function) has been released～","Developers can now bring their own data to customize GPT-3.5 Turbo for their use cases.",{"title":10,"items":189},[190,191,192],{"id":88,"publish_date":89,"is_original":32,"collection":90,"cover_url":91,"cover_url_1_1":92,"title":93,"summary":94,"author":63},{"id":96,"publish_date":97,"is_original":32,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":63},{"id":104,"publish_date":105,"is_original":32,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":63},true,{"code":4,"msg":5,"data":195},{"id":196,"publish_date":197,"is_original":32,"collection":198,"articles_id":199,"cover_url":200,"cover_url_1_1":201,"title":202,"summary":203,"author":63,"content":204},76,"2025-01-07","#AI Video Generation #Google","ybGl8DFjHKjw4PUro9_m3w","article_res/cover/3613b008d99e99c267bce18c2c7f4003.jpeg","article_res/cover/055bce99535eb3d30085f963064e930a.jpeg","Vision Transformer (ViT)","An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","\u003Cdiv class=\"rich_media_content js_underline_content\n                       autoTypeSetting24psection\n            \" id=\"js_content\">\u003Cp style='margin-bottom: 0px;;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>the difference. Today, I learned that the latest Sora-like video generation technology mainly adopts Vision Transformer. I don't really understand it, and I might be explaining it incorrectly; it's mainly for my own learning purposes.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100008915\" data-ratio=\"0.33796296296296297\" data-s=\"300,640\" data-type=\"webp\" data-w=\"1080\" style=\"\" src=\"https://res.cooltool.vip/article_res/assets/17423771980830.01775580687765821.jpeg\">\u003C/p>\u003Ch2 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\";font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Vision Transformer, Overview of ViT\u003C/strong>\u003C/span>\u003C/h2>\u003Cp style='margin-bottom: 0px;;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>It is a model for image classification that processes image patches using an architecture similar to Transformers. ViT was first successfully applied to large-scale image recognition tasks in the paper \"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale\" published by Alexey Dosovitskiy et al. in 2020, showing excellent performance and promoting the development of visual representation learning and modern computer vision.\u003C/p>\u003Ch3 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\";font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Core Concept\u003C/strong>\u003C/span>\u003C/h3>\u003Cul style='margin-top: 8px;margin-bottom: 8px;;padding-left: 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);' class=\"list-paddingleft-1\">\u003Cli style=\";\">\u003Csection style=\";margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">Divide the image into non-overlapping patches of fixed size (e.g., 16x16 pixels), and perform linear embedding after flattening each patch.\u003C/section>\u003C/li>\u003Cli style=\";\">\u003Csection style=\";margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">Add positional encoding to preserve spatial information, as the Transformer itself is not sensitive to the order of arrangement.\u003C/section>\u003C/li>\u003Cli style=\";\">\u003Csection style=\";margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">The sequence of embedded image patches is input into a standard Transformer encoder for processing.\u003C/section>\u003C/li>\u003Cli style=\";\">\u003Csection style=\";margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">A learnable [CLS] token is added to aggregate the full-image information for classification tasks.\u003C/section>\u003C/li>\u003C/ul>\u003Ch3 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\";font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Research Contributions\u003C/strong>\u003C/span>\u003C/h3>\u003Cul style='margin-top: 8px;margin-bottom: 8px;;padding-left: 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);' class=\"list-paddingleft-1\">\u003Cli style=\";\">\u003Csection style=\";margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">It has been proven that without relying on Convolutional Neural Networks (CNNs), a pure Transformer architecture can also achieve excellent performance in image classification tasks.\u003C/section>\u003C/li>\u003Cli style=\";\">\u003Csection style=\";margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">After pre-training on large-scale datasets (such as ImageNet-21k), Vision Transformers (ViT) perform well when transferred to medium and small-scale image recognition benchmarks (such as ImageNet, CIFAR-100, VTAB), while requiring significantly fewer computational resources for training.\u003C/section>\u003C/li>\u003C/ul>\u003Ch2 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\";font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Detailed architecture of ViT\u003C/strong>\u003C/span>\u003C/h2>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100008916\" data-ratio=\"0.7601246105919003\" data-s=\"300,640\" data-type=\"png\" data-w=\"642\" style=\"\" src=\"https://res.cooltool.vip/article_res/assets/17423771981600.187318650988763.png\">\u003C/p>\u003Cspan style=\";font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003C/strong>\u003C/span>\u003Ch3 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\";font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">1. Image processing flow\u003C/strong>\u003C/span>\u003C/h3>\u003Cul style='margin-top: 8px;margin-bottom: 8px;;padding-left: 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);' class=\"list-paddingleft-1\">\u003Cli style=\";\">\u003Csection style=\";margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">Divide the input image into fixed-size, non-overlapping blocks (e.g., 16x16 pixels).\u003C/section>\u003C/li>\u003Cli style=\";\">\u003Csection style=\";margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">Each block is flattened and embedded as a vector through a linear layer.\u003C/section>\u003C/li>\u003Cli style=\";\">\u003Csection style=\";margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">Add absolute position encoding to each block embedding to retain spatial information.\u003C/section>\u003C/li>\u003Cli style=\";\">\u003Csection style=\";margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">Input the sequence of embeddings of all blocks into a standard Transformer encoder.\u003C/section>\u003C/li>\u003C/ul>\u003Ch3 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\";font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">2. Classification mechanism\u003C/strong>\u003C/span>\u003C/h3>\u003Cul style='margin-top: 8px;margin-bottom: 8px;;padding-left: 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);' class=\"list-paddingleft-1\">\u003Cli style=\";\">\u003Csection style=\";margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">A special [CLS] token is added to the input sequence, and after processing by the Transformer encoder, the output vector of this token is used for classification tasks.\u003C/section>\u003C/li>\u003C/ul>\u003Ch2 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\";font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Comparative analysis\u003C/strong>\u003C/span>\u003C/h2>\u003Ch3 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\";font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">a. Architecture and design\u003C/strong>\u003C/span>\u003C/h3>\u003Csection style=';margin-bottom: 0px;overflow-x: auto;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Ctable>\u003Cthead style=\";\">\u003Ctr style=\";\">\u003Cth style=\";line-height: 1.5em;letter-spacing: 0em;text-align: left;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(240, 240, 240);width: auto;height: auto;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;min-width: 85px;\">Characteristics\u003C/th>\u003Cth style=\";line-height: 1.5em;letter-spacing: 0em;text-align: left;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(240, 240, 240);width: auto;height: auto;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;min-width: 85px;\">Vision Transformer (ViT)\u003C/th>\u003Cth style=\";line-height: 1.5em;letter-spacing: 0em;text-align: left;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(240, 240, 240);width: auto;height: auto;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;min-width: 85px;\">Autoregressive Transformer (AR)\u003C/th>\u003Cth style=\";line-height: 1.5em;letter-spacing: 0em;text-align: left;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(240, 240, 240);width: auto;height: auto;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;min-width: 85px;\">Diffusion Transformer (DiT)\u003C/th>\u003C/tr>\u003C/thead>\u003Ctbody style=\";line-height: 1.5em;letter-spacing: 0em;border-width: 0px;border-style: initial;border-color: initial;\">\u003Ctr style=\";background-image: none;background-position: 0% 0%;background-size: auto;background-repeat: no-repeat;background-attachment: scroll;background-origin: padding-box;background-clip: border-box;width: auto;height: auto;\">\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Data processing\u003C/strong>\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Treating images as a sequence of patches\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Processing sequence data (text, images)\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Modeling data through noise perturbation and denoising\u003C/td>\u003C/tr>\u003Ctr style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(248, 248, 248);width: auto;height: auto;\">\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Positional encoding\u003C/strong>\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Crucial for spatial information\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Critical for maintaining sequence order\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Used to maintain structure during the diffusion process\u003C/td>\u003C/tr>\u003Ctr style=\";background-image: none;background-position: 0% 0%;background-size: auto;background-repeat: no-repeat;background-attachment: scroll;background-origin: padding-box;background-clip: border-box;width: auto;height: auto;\">\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Model components\u003C/strong>\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Block embedding, Transformer encoder\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Masked self-attention, Transformer decoder\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Transformer layers in diffusion steps\u003C/td>\u003C/tr>\u003Ctr style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(248, 248, 248);width: auto;height: auto;\">\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Generation capability\u003C/strong>\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Limited (mainly used for discriminative tasks)\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Strong generation capability\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Strong generation capability, with high fidelity\u003C/td>\u003C/tr>\u003C/tbody>\u003C/table>\u003C/section>\u003Ch3 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\";font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">b. Application fields\u003C/strong>\u003C/span>\u003C/h3>\u003Csection style=';margin-bottom: 0px;overflow-x: auto;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Ctable>\u003Cthead style=\";\">\u003Ctr style=\";\">\u003Cth style=\";line-height: 1.5em;letter-spacing: 0em;text-align: left;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(240, 240, 240);width: auto;height: auto;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;min-width: 85px;\">Application Field\u003C/th>\u003Cth style=\";line-height: 1.5em;letter-spacing: 0em;text-align: left;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(240, 240, 240);width: auto;height: auto;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;min-width: 85px;\">ViT\u003C/th>\u003Cth style=\";line-height: 1.5em;letter-spacing: 0em;text-align: left;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(240, 240, 240);width: auto;height: auto;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;min-width: 85px;\">Autoregressive Transformer (AR)\u003C/th>\u003Cth style=\";line-height: 1.5em;letter-spacing: 0em;text-align: left;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(240, 240, 240);width: auto;height: auto;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;min-width: 85px;\">Diffusion Transformer (DiT)\u003C/th>\u003C/tr>\u003C/thead>\u003Ctbody style=\";line-height: 1.5em;letter-spacing: 0em;border-width: 0px;border-style: initial;border-color: initial;\">\u003Ctr style=\";background-image: none;background-position: 0% 0%;background-size: auto;background-repeat: no-repeat;background-attachment: scroll;background-origin: padding-box;background-clip: border-box;width: auto;height: auto;\">\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Image Classification\u003C/strong>\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Main Purpose\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Less common, may be achievable on image sequences\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Typically not used for classification tasks\u003C/td>\u003C/tr>\u003Ctr style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(248, 248, 248);width: auto;height: auto;\">\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Image generation\u003C/strong>\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Limited, requires modification\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Effective when images are treated as sequences\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Highly efficient and state-of-the-art quality\u003C/td>\u003C/tr>\u003Ctr style=\";background-image: none;background-position: 0% 0%;background-size: auto;background-repeat: no-repeat;background-attachment: scroll;background-origin: padding-box;background-clip: border-box;width: auto;height: auto;\">\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Natural Language Processing\u003C/strong>\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Not directly applicable\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Core applications (such as GPT models)\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">With more limitations, unless integrated into multimodal models\u003C/td>\u003C/tr>\u003Ctr style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(248, 248, 248);width: auto;height: auto;\">\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Other fields\u003C/strong>\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Object detection, segmentation\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Music generation, code generation, etc.\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Audio synthesis, video generation, etc.\u003C/td>\u003C/tr>\u003C/tbody>\u003C/table>\u003C/section>\u003Ch3 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\";font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">c. Advantages and merits\u003C/strong>\u003C/span>\u003C/h3>\u003Csection style=';margin-bottom: 0px;overflow-x: auto;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Ctable>\u003Cthead style=\";\">\u003Ctr style=\";\">\u003Cth style=\";line-height: 1.5em;letter-spacing: 0em;text-align: left;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(240, 240, 240);width: auto;height: auto;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;min-width: 85px;\">Aspect\u003C/th>\u003Cth style=\";line-height: 1.5em;letter-spacing: 0em;text-align: left;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(240, 240, 240);width: auto;height: auto;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;min-width: 85px;\">ViT\u003C/th>\u003Cth style=\";line-height: 1.5em;letter-spacing: 0em;text-align: left;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(240, 240, 240);width: auto;height: auto;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;min-width: 85px;\">Autoregressive Transformer (AR)\u003C/th>\u003Cth style=\";line-height: 1.5em;letter-spacing: 0em;text-align: left;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(240, 240, 240);width: auto;height: auto;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;min-width: 85px;\">Diffusion Transformer (DiT)\u003C/th>\u003C/tr>\u003C/thead>\u003Ctbody style=\";line-height: 1.5em;letter-spacing: 0em;border-width: 0px;border-style: initial;border-color: initial;\">\u003Ctr style=\";background-image: none;background-position: 0% 0%;background-size: auto;background-repeat: no-repeat;background-attachment: scroll;background-origin: padding-box;background-clip: border-box;width: auto;height: auto;\">\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Performance\u003C/strong>\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Compete with CNNs in visual tasks\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Superior performance in generation tasks\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Leading in high-fidelity generation\u003C/td>\u003C/tr>\u003Ctr style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(248, 248, 248);width: auto;height: auto;\">\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Scalability\u003C/strong>\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Scales well with increasing data and model size\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Highly scalable, benefiting from large-scale datasets\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Extensible, but computationally intensive due to the multi-step diffusion process\u003C/td>\u003C/tr>\u003Ctr style=\";background-image: none;background-position: 0% 0%;background-size: auto;background-repeat: no-repeat;background-attachment: scroll;background-origin: padding-box;background-clip: border-box;width: auto;height: auto;\">\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Flexibility\u003C/strong>\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Mainly used for visual tasks, adaptable to some tasks\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Multifunctionality across multiple domains\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Mainly used for generative tasks, can be adapted through conditioning\u003C/td>\u003C/tr>\u003Ctr style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(248, 248, 248);width: auto;height: auto;\">\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Interpretability\u003C/strong>\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">The block-based method provides a certain level of interpretability\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">The sequential nature helps in understanding the generation process\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Due to the complexity of the diffusion process, it is more difficult to explain\u003C/td>\u003C/tr>\u003C/tbody>\u003C/table>\u003C/section>\u003Ch3 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\";font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">d. Limitations and Challenges\u003C/strong>\u003C/span>\u003C/h3>\u003Csection style=';margin-bottom: 0px;overflow-x: auto;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Ctable>\u003Cthead style=\";\">\u003Ctr style=\";\">\u003Cth style=\";line-height: 1.5em;letter-spacing: 0em;text-align: left;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(240, 240, 240);width: auto;height: auto;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;min-width: 85px;\">Aspect\u003C/th>\u003Cth style=\";line-height: 1.5em;letter-spacing: 0em;text-align: left;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(240, 240, 240);width: auto;height: auto;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;min-width: 85px;\">ViT\u003C/th>\u003Cth style=\";line-height: 1.5em;letter-spacing: 0em;text-align: left;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(240, 240, 240);width: auto;height: auto;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;min-width: 85px;\">Autoregressive Transformer (AR)\u003C/th>\u003Cth style=\";line-height: 1.5em;letter-spacing: 0em;text-align: left;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(240, 240, 240);width: auto;height: auto;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;min-width: 85px;\">Diffusion Transformer (DiT)\u003C/th>\u003C/tr>\u003C/thead>\u003Ctbody style=\";line-height: 1.5em;letter-spacing: 0em;border-width: 0px;border-style: initial;border-color: initial;\">\u003Ctr style=\";background-image: none;background-position: 0% 0%;background-size: auto;background-repeat: no-repeat;background-attachment: scroll;background-origin: padding-box;background-clip: border-box;width: auto;height: auto;\">\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Data efficiency\u003C/strong>\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Requires a large amount of data to perform well\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">May require a large amount of data, especially for long sequences\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Extremely requires data and computational resources\u003C/td>\u003C/tr>\u003Ctr style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(248, 248, 248);width: auto;height: auto;\">\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Computational cost\u003C/strong>\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Due to the Transformer layers, especially for high-resolution images, the computational cost is high\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Due to self-attention, the computational cost is high for long sequences\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">The computational cost is very high due to the iterative denoising steps.\u003C/td>\u003C/tr>\u003Ctr style=\";background-image: none;background-position: 0% 0%;background-size: auto;background-repeat: no-repeat;background-attachment: scroll;background-origin: padding-box;background-clip: border-box;width: auto;height: auto;\">\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Training complexity\u003C/strong>\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Training from scratch without pretraining can be challenging.\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Requires careful handling of sequence length and masking.\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Training is complex due to the dual process (diffusion and Transformer).\u003C/td>\u003C/tr>\u003Ctr style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(248, 248, 248);width: auto;height: auto;\">\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">\u003Cstrong style=\";background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Generation quality\u003C/strong>\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Limited compared to specialized generative models\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">Without sufficient training, it may be difficult to achieve high-fidelity generation\u003C/td>\u003Ctd style=\";min-width: 85px;border-color: rgba(204, 204, 204, 0.4);border-radius: 0px;\">May produce artifacts if improperly trained, but generally the quality is high\u003C/td>\u003C/tr>\u003C/tbody>\u003C/table>\u003C/section>\u003Cp>\u003Cbr>\u003C/p>\u003Cp style=\"display: none;\">\u003Cmp-style-type data-value=\"3\">\u003C/mp-style-type>\u003C/p>\u003C/div>",1752585424225]