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We introduce Contextual Vision Transformers (ContextViT), a method designed to generate robust image representations for datasets experiencing shifts in latent factors across various groups.
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What do vision transformers learn? a visual exploration
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Finetune like you pretrain: Improved finetuning of zero-shot vision models
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Emerging properties in self-supervised vision transformers
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
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On the robustness of vision transformers to adversarial examples
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Learning transferable visual models from natural language supervision
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Incorporating knowledge of plates in batch normalization improves generalization of deep learning for microscopy images
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Learning representations for image-based profiling of perturbations
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Machine learning enabled pooled optical screening in human lung cancer cells
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Mixed-effects transformers for hierarchical adaptation, 2022
J. White, N. Goodman, and R. Hawkins · 2022
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
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An explanation of in-context learning as implicit bayesian inference, 2022
S. M. Xie, A. Raghunathan, P. Liang, and T. Ma · 2022
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L. Xu, W. Ouyang, M. Bennamoun, F. Boussaid, and D. Xu · 2022
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Memo: Test time robustness via adaptation and augmentation
M. Zhang, S. Levine, and C. Finn · 2022
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Albumentations: Fast and flexible image augmentations
A. Buslaev, V. I. Iglovikov, E. Khvedchenya, A. Parinov, M. Druzhinin, and A. A. Kalinin · 2078
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