![]() In language, unsupervised learning algorithms that rely on word prediction (like GPT-2 and BERT) have been extremely successful, achieving top performance on a wide array of language tasks. However, our results suggest that when faced with a new domain where the correct model priors are unknown, a large GPT-2 can learn excellent features without the need for domain-specific architectural design choices. ![]() As a consequence, we require significantly more compute in order to produce features competitive with those from top unsupervised convolutional nets. To highlight the potential of generative sequence modeling as a general purpose unsupervised learning algorithm, we deliberately use the same transformer architecture as GPT-2 in language. On JFT (300M images with 18K classes), achieved a result of We only show ImageNet linear probe accuracy for iGPT-XL since otherÄ®xperiments did not finish before we needed to transition to different Logistic regression on learned features (linear probe) As further proof, features from the model achieve state-of-the-art performance on a number of classification datasets and near state-of-the-art unsupervised accuracy on ImageNet. This is evidenced by the diverse range of coherent image samples it generates, even without the guidance of human provided labels. When we train GPT-2 on images unrolled into long sequences of pixels, which we call iGPT, we find that the model appears to understand 2-D image characteristics such as object appearance and category. Transformer models like BERT and GPT-2 are domain agnostic, meaning that they can be directly applied to 1-D sequences of any form. Our work aims to understand and bridge this gap. However, the same broad class of models has not been successful in producing strong features for image classification. Recently, it has seen incredible success in language, as transformer models like BERT, GPT-2, RoBERTa, T5, and other variants have achieved top performance on a wide array of language tasks. We will respond as quickly as possible.Unsupervised and self-supervised learning, or learning without human-labeled data, is a longstanding challenge of machine learning. Please include your ip address, the time you received this message and what you were doing at the time. You can reach us at management (at) e621 (dot) net You will need to contact us to have it removed. If none of the above apply to you, then you have likely been blocked manually after review. If you repeatedly see this message you should stop all access attempts and contact us so that the problem can be resolved. #4 Something else, probably abusive behavior Please see the wiki page for the API for additional details. The default user agent for many programming languages and libraries have been blocked for abuse.ÄO NOT USE A BROWSER USER AGENT FOR API TOOLS - YOU WILL GET MANUALLY BLOCKED. If you are attempting to use the API you may be seeing this if your user agent is not in line with our user agent policy. Please contact the author, or find another, more up to date tool. You likely can't resolve this problem yourself, as the application you are using needs to be updated. Do not use some random "documentation" you found on github, it is out of date and incorrect. Please visit the wiki for the most up to date information. If you are making requests to endpoints that are not plurals, then you fall under this category. You need to update your application to use the new API urls and response formats. As the result of no shared code between the old and new site, the API changed, both in response format and urls. We updated our codebase quite drastically in March 2020.
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