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Modern image classification is based upon directly predicting classes via large discriminative networks, which do not directly contain information about the intuitive visual features that may constitute a classification decision.
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Imagenet: A large-scale hierarchical image database
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum, and Boris Katz · 2019
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Angus Galloway, Anna Golubeva, Mahmoud Salem, Mihai Nica, Yani Ioannou, and Graham W Taylor · 2022
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Finetune like you pretrain: Improved finetuning of zero-shot vision models
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Fine-tuning can distort pretrained features and underperform out-of-distribution
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Invariance principle meets information bottleneck for out-of-distribution generalization
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Learning invariant representations and risks for semi-supervised domain adaptation
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Learning transferable visual models from natural language supervision
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Invariant information bottleneck for domain generalization
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Prompt distribution learning
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Visual classification via description from large language models
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What does a platypus look like? generating customized prompts for zero-shot image classification
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Robust fine-tuning of zero-shot models
Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, et al · 2022
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Yue Yang, Artemis Panagopoulou, Shenghao Zhou, Daniel Jin, Chris Callison-Burch, and Mark Yatskar · 2022
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Fundamental limits and tradeoffs in invariant representation learning
Han Zhao, Chen Dan, Bryon Aragam, Tommi S Jaakkola, Geoffrey J Gordon, and Pradeep Ravikumar · 2022
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Prompt-aligned gradient for prompt tuning
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Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery
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