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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

, , , , , , , , , , , and . 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021, OpenReview.net, (2021)

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Scaling Vision Transformers to 22 Billion Parameters., , , , , , , , , and 32 other author(s). CoRR, (2023)Knowledge distillation: A good teacher is patient and consistent., , , , , and . CoRR, (2021)Semi-Supervised Cross-Media Feature Learning With Unified Patch Graph Regularization., , , and . IEEE Trans. Circuits Syst. Video Techn., 26 (3): 583-596 (2016)SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size., , , , , , , , and . CoRR, (2021)CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?, , , , , and . CoRR, (2024)How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers., , , , , and . CoRR, (2021)Better plain ViT baselines for ImageNet-1k., , and . CoRR, (2022)Big Transfer (BiT): General Visual Representation Learning., , , , , , and . ECCV (5), volume 12350 of Lecture Notes in Computer Science, page 491-507. Springer, (2020)Heterogeneous Metric Learning with Joint Graph Regularization for Cross-Media Retrieval., , and . AAAI, page 1198-1204. AAAI Press, (2013)Revisiting the Calibration of Modern Neural Networks., , , , , , , and . NeurIPS, page 15682-15694. (2021)