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We propose Neural Priming, a technique for adapting large pretrained models to distribution shifts and downstream tasks given few or no labeled examples.
Facilitation in recognizing pairs of words: evidence of a dependence between retrieval operations
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Automated flower classification over a large number of classes
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Sun database: Large-scale scene recognition from abbey to zoo
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Cats and dogs
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Fine-grained visual classification of aircraft
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Model-agnostic meta-learning for fast adaptation of deep networks
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Prototypical networks for few-shot learning
J. Snell, K. Swersky, and R. Zemel · 2017
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Tadam: Task dependent adaptive metric for improved few-shot learning
B. Oreshkin, P. Rodríguez López, and A. Lacoste · 2018
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Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
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Rethinking imagenet pre-training
K. He, R. Girshick, and P. Dollár · 2019
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Task agnostic meta-learning for few-shot learning
M. A. Jamal and G.-J. Qi · 2019
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Billion-scale similarity search with GPUs
J. Johnson, M. Douze, and H. Jégou · 2019
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Generalization through memorization: Nearest neighbor language models
U. Khandelwal, O. Levy, D. Jurafsky, L. Zettlemoyer, and M. Lewis · 2019
Learning transferable visual models from natural language supervision
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Improving language models by retrieving from trillions of tokens
S. Borgeaud, A. Mensch, J. Hoffmann, T. Cai, E. Rutherford, K. Millican, G. B. Van Den Driessche, J.-B. Lespiau, B. Damoc, A. Clark, et al · 2022
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Data determines distributional robustness in contrastive language image pre-training (CLIP)
A. Fang, G. Ilharco, M. Wortsman, Y. Wan, V. Shankar, A. Dave, and L. Schmidt · 2022
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Test-time training with masked autoencoders
Y. Gandelsman, Y. Sun, X. Chen, and A. Efros · 2022
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Finetune like you pretrain: Improved finetuning of zero-shot vision models
S. Goyal, A. Kumar, S. Garg, Z. Kolter, and A. Raghunathan · 2022
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Do imagenet classifiers generalize to imagenet?
B. Recht, R. Roelofs, L. Schmidt, and V. Shankar · 2019
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Learning robust global representations by penalizing local predictive power
H. Wang, S. Ge, Z. C. Lipton, and E. P. Xing · 2019
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An image is worth 16x16 words: Transformers for image recognition at scale
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al · 2020
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Generalizing to generalize: Humans flexibly switch between compositional and conjunctive structures during reinforcement learning
N. T. Franklin and M. J. Frank · 2020
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Retrieval augmented language model pre-training
K. Guu, K. Lee, Z. Tung, P. Pasupat, and M. Chang · 2020
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Test-time training with self-supervision for generalization under distribution shifts
Y. Sun, X. Wang, Z. Liu, J. Miller, A. Efros, and M. Hardt · 2020
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Fine-tuning can distort pretrained features and underperform out-of-distribution
A. Kumar, A. Raghunathan, R. M. Jones, T. Ma, and P. Liang · 2022
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Surgical fine-tuning improves adaptation to distribution shifts
Y. Lee, A. S. Chen, F. Tajwar, A. Kumar, H. Yao, P. Liang, and C. Finn · 2022
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Visual classification via description from large language models
S. Menon and C. Vondrick · 2022
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What does a platypus look like? generating customized prompts for zero-shot image classification
S. Pratt, R. Liu, and A. Farhadi · 2022
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Laion-5b: An open large-scale dataset for training next generation image-text models
C. Schuhmann, R. Beaumont, R. Vencu, C. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortsman, et al · 2022
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Test-time prompt tuning for zero-shot generalization in vision-language models
M. Shu, W. Nie, D.-A. Huang, Z. Yu, T. Goldstein, A. Anandkumar, and C. Xiao · 2022
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Sus-x: Training-free name-only transfer of vision-language models
V. Udandarao, A. Gupta, and S. Albanie · 2022
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Robust fine-tuning of zero-shot models
M. Wortsman, G. Ilharco, J. W. Kim, M. Li, S. Kornblith, R. Roelofs, R. G. Lopes, H. Hajishirzi, A. Farhadi, H. Namkoong, et al · 2022
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Tip-adapter: Training-free adaption of clip for few-shot classification
R. Zhang, W. Zhang, R. Fang, P. Gao, K. Li, J. Dai, Y. Qiao, and H. Li · 2022
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Learning to prompt for vision-language models
K. Zhou, J. Yang, C. C. Loy, and Z. Liu · 2022
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Learning customized visual models with retrieval-augmented knowledge
H. Liu, K. Son, J. Yang, C. Liu, J. Gao, Y. J. Lee, and C. Li · 2023
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Adanns: A framework for adaptive semantic search
A. Rege, A. Kusupati, A. Fan, Q. Cao, S. Kakade, P. Jain, A. Farhadi, et al · 2023
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