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Out-of-distribution (OOD) generalization, where the model needs to handle distribution shifts from training, is a major challenge of machine learning.
Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Fei-Fei, L., Fergus, R., and Perona, P · 2004
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Automated flower classification over a large number of classes
Nilsback, M.-E. and Zisserman, A · 2008
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Sun database: Large-scale scene recognition from abbey to zoo
Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., and Torralba, A · 2010
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Unbiased look at dataset bias
Torralba, A. and Efros, A. A · 2011
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Undoing the damage of dataset bias
Khosla, A., Zhou, T., Malisiewicz, T., Efros, A. A., and Torralba, A · 2012
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Cats and dogs
Parkhi, O. M., Vedaldi, A., Zisserman, A., and Jawahar, C · 2012
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Ucf101: A dataset of 101 human actions classes from videos in the wild
Soomro, K., Zamir, A. R., and Shah, M · 2012
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Write a classifier: Zero-shot learning using purely textual descriptions
Elhoseiny, M., Saleh, B., and Elgammal, A · 2013
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Devise: A deep visual-semantic embedding model
Frome, A., Corrado, G. S., Shlens, J., Bengio, S., Dean, J., Ranzato, M., and Mikolov, T · 2013
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3d object representations for fine-grained categorization
Krause, J., Stark, M., Deng, J., and Fei-Fei, L · 2013
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Fine-grained visual classification of aircraft
Maji, S., Rahtu, E., Kannala, J., Blaschko, M., and Vedaldi, A · 2013
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Domain generalization via invariant feature representation
Muandet, K., Balduzzi, D., and Schölkopf, B · 2013
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Zero-shot learning through cross-modal transfer
Socher, R., Ganjoo, M., Manning, C. D., and Ng, A · 2013
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Analyzing the performance of multilayer neural networks for object recognition
Agrawal, P., Girshick, R., and Malik, J · 2014
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Food-101–mining discriminative components with random forests
Bossard, L., Guillaumin, M., and Van Gool, L · 2014
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Describing textures in the wild
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., and Vedaldi, A · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., and Malik, J · 2014
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Deep coral: Correlation alignment for deep domain adaptation
Sun, B. and Saenko, K · 2016
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Deeper, broader and artier domain generalization
Li, D., Yang, Y., Song, Y.-Z., and Hospedales, T. M · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Deep hashing network for unsupervised domain adaptation
Venkateswara, H., Eusebio, J., Chakraborty, S., and Panchanathan, S · 2017
Cited alongside, same era.
Metareg: Towards domain generalization using meta-regularization
Balaji, Y., Sankaranarayanan, S., and Chellappa, R · 2018
Cited alongside, same era.
Recognition in terra incognita
Beery, S., Van Horn, G., and Perona, P · 2018
Cited alongside, same era.
Generalizing to unseen domains via adversarial data augmentation
Volpi, R., Namkoong, H., Sener, O., Duchi, J. C., Murino, V., and Savarese, S · 2018
Cited alongside, same era.
Domain generalization by solving jigsaw puzzles
Carlucci, F. M., D’Innocente, A., Bucci, S., Caputo, B., and Tommasi, T · 2019
Cited alongside, same era.
Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Helber, P., Bischke, B., Dengel, A., and Borth, D · 2019
Clip-adapter: Better vision-language models with feature adapters
Gao, P., Geng, S., Zhang, R., Ma, T., Fang, R., Zhang, Y., Li, H., and Qiao, Y · 2021
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In search of lost domain generalization
Gulrajani, I. and Lopez-Paz, D · 2021
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Scaling up visual and vision-language representation learning with noisy text supervision
Jia, C., Yang, Y., Xia, Y., Chen, Y.-T., Parekh, Z., Pham, H., Le, Q., Sung, Y.-H., Li, Z., and Duerig, T · 2021
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Vilt: Vision-and-language transformer without convolution or region supervision
Kim, W., Son, B., and Kim, I · 2021
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Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm
Li, Y., Liang, F., Zhao, L., Cui, Y., Ouyang, W., Shao, J., Yu, F., and Yan, J · 2021
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Cited alongside, same era.
Do better imagenet models transfer better?
Kornblith, S., Shlens, J., and Le, Q. V · 2019
Cited alongside, same era.
Feature-critic networks for heterogeneous domain generalization
Li, Y., Yang, Y., Zhou, W., and Hospedales, T. M · 2019
Cited alongside, same era.
Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2019
Cited alongside, same era.
Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Lu, J., Batra, D., Parikh, D., and Lee, S · 2019
Cited alongside, same era.
Moment matching for multi-source domain adaptation
Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., and Wang, B · 2019
Cited alongside, same era.
Do imagenet classifiers generalize to imagenet?
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 2019
Cited alongside, same era.
Miller, J. P., Taori, R., Raghunathan, A., Sagawa, S., Koh, P. W., Shankar, V., Liang, P., Carmon, Y., and Schmidt, L · 2021
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Combined scaling for zero-shot transfer learning
Pham, H., Dai, Z., Ghiasi, G., Liu, H., Yu, A. W., Luong, M.-T., Tan, M., and Le, Q. V · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
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Open domain generalization with domain-augmented meta-learning
Shu, Y., Cao, Z., Wang, C., Wang, J., and Long, M · 2021
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Tip-adapter: Training-free clip-adapter for better vision-language modeling
Zhang, R., Fang, R., Zhang, W., Gao, P., Li, K., Dai, J., Qiao, Y., and Li, H · 2021
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Learning to prompt for vision-language models
Zhou, K., Yang, J., Loy, C. C., and Liu, Z · 2021
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Domain generalization by mutual-information regularization with pre-trained models
Cha, J., Lee, K., Park, S., and Chun, S · 2022
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Cyclip: Cyclic contrastive language-image pretraining
Goel, S., Bansal, H., Bhatia, S., Rossi, R. A., Vinay, V., and Grover, A · 2022
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Fine-tuning can distort pretrained features and underperform out-of-distribution
Kumar, A., Raghunathan, A., Jones, R., Ma, T., and Liang, P · 2022
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Prompt distribution learning
Lu, Y., Liu, J., Zhang, Y., Liu, Y., and Tian, X · 2022
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Slip: Self-supervision meets language-image pre-training
Mu, N., Kirillov, A., Wagner, D., and Xie, S · 2022
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Test-time prompt tuning for zero-shot generalization in vision-language models
Shu, M., Nie, W., Huang, D.-A., Yu, Z., Goldstein, T., Anandkumar, A., and Xiao, C · 2022
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Robust fine-tuning of zero-shot models
Wortsman, M., Ilharco, G., Kim, J. W., Li, M., Kornblith, S., Roelofs, R., Lopes, R. G., Hajishirzi, H., Farhadi, A., Namkoong, H., et al · 2022
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Lit: Zero-shot transfer with locked-image text tuning
Zhai, X., Wang, X., Mustafa, B., Steiner, A., Keysers, D., Kolesnikov, A., and Beyer, L · 2022
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Domain prompt learning for efficiently adapting clip to unseen domains
Zhang, X., Gu, S. S., Matsuo, Y., and Iwasawa, Y · 2022
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Finetune like you pretrain: Improved finetuning of zero-shot vision models
Goyal, S., Kumar, A., Garg, S., Kolter, Z., and Raghunathan, A · 2023
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