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• W3048911294 abstract "Recent self-supervised contrastive methods have been able to produce impressive transferable visual representations by learning to be invariant to different data augmentations. However, these methods implicitly assume a particular set of representational invariances (e.g., invariance to color), and can perform poorly when a downstream task violates this assumption (e.g., distinguishing red vs. yellow cars). We introduce a contrastive learning framework which does not require prior knowledge of specific, task-dependent invariances. Our model learns to capture varying and invariant factors for visual representations by constructing separate embedding spaces, each of which is invariant to all but one augmentation. We use a multi-head network with a shared backbone which captures information across each augmentation and alone outperforms all baselines on downstream tasks. We further find that the concatenation of the invariant and varying spaces performs best across all tasks we investigate, including coarse-grained, fine-grained, and few-shot downstream classification tasks, and various data corruptions." @default.
• W3048911294 created "2020-08-18" @default.
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• W3048911294 date "2020-08-13" @default.
• W3048911294 modified "2023-09-23" @default.
• W3048911294 title "What Should Not Be Contrastive in Contrastive Learning" @default.
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• W3048911294 hasPublicationYear "2020" @default.
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