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While large pretrained foundation models (FMs) have shown remarkable zero-shot classification robustness to dataset-level distribution shifts, their robustness to subpopulation or group shifts is relatively underexplored.
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Scaling up visual and vision-language representation learning with noisy text supervision
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Wilds: A benchmark of in-the-wild distribution shifts
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Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm
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Just train twice: Improving group robustness without training group information
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Exactly computing the local lipschitz constant of relu networks
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Hidden stratification causes clinically meaningful failures in machine learning for medical imaging
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Robust fine-tuning of zero-shot models
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Tip-adapter: Training-free clip-adapter for better vision-language modeling
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Learning to prompt for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2021
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Visual prompting: Modifying pixel space to adapt pre-trained models
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Last layer re-training is sufficient for robustness to spurious correlations
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Fine-tuning can distort pretrained features and underperform out-of-distribution
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Core risk minimization using salient imagenet
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Correct-n-contrast: A contrastive approach for improving robustness to spurious correlations
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Conditional prompt learning for vision-language models
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