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Foundation models are usually pre-trained on large-scale datasets and then adapted to downstream tasks through tuning.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
L. Fei-Fei, R. Fergus, and P. Perona, “Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,” Computer Vision and Pattern Recognition Workshop , 2004
2004
Earlier work this paper cites.
M.-E. Nilsback and A. Zisserman, “Automated flower classification over a large number of classes,” in 2008 Sixth Indian conference on computer vision, graphics & image processing . IEEE, 2008, pp. 722–729
2008
Earlier work this paper cites.
A. Krizhevsky, G. Hinton et al. , “Learning multiple layers of features from tiny images,” 2009
2009
Earlier work this paper cites.
S. Bird, E. Klein, and E. Loper, Natural language processing with Python: analyzing text with the natural language toolkit . ” O’Reilly Media, Inc.”, 2009
2009
Earlier work this paper cites.
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, “Reading digits in natural images with unsupervised feature learning,” in NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011 , 2011
2011
Earlier work this paper cites.
O. M. Parkhi, A. Vedaldi, A. Zisserman, and C. V. Jawahar, “Cats and dogs,” in IEEE Conference on Computer Vision and Pattern Recognition , 2012
2012
Earlier work this paper cites.
J. Krause, M. Stark, J. Deng, and L. Fei-Fei, “3d object representations for fine-grained categorization,” in 2013 IEEE International Conference on Computer Vision Workshops , 2013, pp. 554–561
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Ng, and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank,” in Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing . Seattle, Washington, USA: Association for Computational Linguistics, Oct. 2013, pp. 1631–1642
2013
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13 . Springer, 2014, pp. 740–755
2014
Earlier work this paper cites.
L. Bossard, M. Guillaumin, and L. Van Gool, “Food-101 – mining discriminative components with random forests,” in European Conference on Computer Vision , 2014
2014
Earlier work this paper cites.
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, , and A. Vedaldi, “Describing textures in the wild,” in Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Advances in neural information processing systems , vol. 28, 2015
2015
Earlier work this paper cites.
T. Xiao, T. Xia, Y. Yang, C. Huang, and X. Wang, “Learning from massive noisy labeled data for image classification,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2015, pp. 2691–2699
2015
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision (IJCV) , vol. 115, no. 3, pp. 211–252, 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Y. Zhu, R. Kiros, R. Zemel, R. Salakhutdinov, R. Urtasun, A. Torralba, and S. Fidler, “Aligning books and movies: Towards story-like visual explanations by watching movies and reading books,” 2015 IEEE International Conference on Computer Vision (ICCV) , Dec 2015
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016
2016
Earlier work this paper cites.
B. Thomee, D. A. Shamma, G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and L.-J. Li, “Yfcc100m,” Communications of the ACM , vol. 59, no. 2, p. 64–73, Jan 2016
2016
Earlier work this paper cites.
J. Goldberger and E. Ben-Reuven, “Training deep neural-networks using a noise adaptation layer,” in International Conference on Learning Representations (ICLR) , 2016
2016
Earlier work this paper cites.
G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Q. Weinberger, “Deep networks with stochastic depth,” in Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14 . Springer, 2016, pp. 646–661
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14 . Springer, 2016, pp. 630–645
2016
Earlier work this paper cites.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2980–2988
2017
Earlier work this paper cites.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2961–2969
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Ghosh, H. Kumar, and P. S. Sastry, “Robust loss functions under label noise for deep neural networks,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , 2017
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
G. Cheng, J. Han, and X. Lu, “Remote sensing image scene classification: Benchmark and state of the art,” Proceedings of the IEEE , vol. 105, no. 10, pp. 1865–1883, Oct 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1251–1258
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever et al. , “Improving language understanding by generative pre-training,” 2018
2018
Earlier work this paper cites.
Z. Zhang and M. Sabuncu, “Generalized cross entropy loss for training deep neural networks with noisy labels,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 31, 2018
2018
Earlier work this paper cites.
B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu, I. W.-H. Tsang, and M. Sugiyama, “Co-teaching: Robust training of deep neural networks with extremely noisy labels,” Advances in Neural Information Processing Systems (NeurIPS) , 2018
2018
Earlier work this paper cites.
P. Helber, B. Bischke, A. Dengel, and D. Borth, “Introducing eurosat: A novel dataset and deep learning benchmark for land use and land cover classification,” in IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium . IEEE, 2018, pp. 204–207
2018
Earlier work this paper cites.
B. S. Veeling, J. Linmans, J. Winkens, T. Cohen, and M. Welling, “Rotation equivariant CNNs for digital pathology,” Jun. 2018
2018
Earlier work this paper cites.
A. Wang, A. Singh, J. Michael, F. Hill, O. Levy, and S. Bowman, “Glue: A multi-task benchmark and analysis platform for natural language understanding,” Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP , 2018
2018
Earlier work this paper cites.
S. Kornblith, J. Shlens, and Q. V. Le, “Do better imagenet models transfer better?” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, Jun 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Earlier work this paper cites.
Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. R. Salakhutdinov, and Q. V. Le, “Xlnet: Generalized autoregressive pretraining for language understanding,” Advances in neural information processing systems , vol. 32, 2019
2019
Earlier work this paper cites.
X. Chen, S. Wang, M. Long, and J. Wang, “Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation,” in Proceedings of the 36th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, K. Chaudhuri and R. Salakhutdinov, Eds., vol. 97. PMLR, 09–15 Jun 2019, pp. 1081–1090
2019
Earlier work this paper cites.
Y. Wang, X. Ma, Z. Chen, Y. Luo, J. Yi, and J. Bailey, “Symmetric cross entropy for robust learning with noisy labels,” Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , pp. 322–330, 2019
2019
Earlier work this paper cites.
B. Kang, S. Xie, M. Rohrbach, Z. Yan, A. Gordo, J. Feng, and Y. Kalantidis, “Decoupling representation and classifier for long-tailed recognition,” in International Conference on Learning Representations , 2019
2019
Earlier work this paper cites.
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” in International Conference on Machine Learning . PMLR, 2019, pp. 2790–2799
2019
Earlier work this paper cites.
B. Recht, R. Roelofs, L. Schmidt, and V. Shankar, “Do imagenet classifiers generalize to imagenet?” in International conference on machine learning . PMLR, 2019, pp. 5389–5400
2019
Earlier work this paper cites.
P. Nakkiran, G. Kaplun, Y. Bansal, T. Yang, B. Barak, and I. Sutskever, “Deep double descent: where bigger models and more data hurt,” Journal of Statistical Mechanics: Theory and Experiment , vol. 2021, 2019. [Online]. Available: https://api.semanticscholar.org/CorpusID:207808916
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
T. He, Z. Zhang, H. Zhang, Z. Zhang, J. Xie, and M. Li, “Bag of tricks for image classification with convolutional neural networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2019
2019
Earlier work this paper cites.
2019
Cited alongside, same era.
S. Yun, D. Han, S. J. Oh, S. Chun, J. Choe, and Y. Yoo, “Cutmix: Regularization strategy to train strong classifiers with localizable features,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 6023–6032
2019
Cited alongside, same era.
——, “Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2019
2019
Cited alongside, same era.
X. Peng, Q. Bai, X. Xia, Z. Huang, K. Saenko, and B. Wang, “Moment matching for multi-source domain adaptation,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 1406–1415
2019
Cited alongside, same era.
C. Schuhmann, R. Beaumont, R. Vencu, C. W. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortsman, P. Schramowski, S. R. Kundurthy, K. Crowson, L. Schmidt, R. Kaczmarczyk, and J. Jitsev, “LAION-5b: An open large-scale dataset for training next generation image-text models,” in Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track , 2022
2022
Later among the works it cites.
Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, “A convnet for the 2020s,” 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Jun 2022
2022
Later among the works it cites.
M. Byeon, B. Park, H. Kim, S. Lee, W. Baek, and S. Kim, “Coyo-700m: Image-text pair dataset,” https://github.com/kakaobrain/coyo-dataset , 2022
2022
Later among the works it cites.
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H. Wang, S. Ge, E. P. Xing, and Z. C. Lipton, “Learning robust global representations by penalizing local predictive power,” in Advances in Neural Information Processing Systems (NeurIPS) , 2019
2019
Cited alongside, same era.
A. Barbu, D. Mayo, J. Alverio, W. Luo, C. Wang, D. Gutfreund, J. Tenenbaum, and B. Katz, “Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models,” in Advances in Neural Information Processing Systems , H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, Eds., vol. 32, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in International conference on machine learning . PMLR, 2019, pp. 6105–6114
2019
Cited alongside, same era.
R. Wightman, “Pytorch image models,” https://github.com/rwightman/pytorch-image-models , 2019
2019
Cited alongside, same era.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
A. Kolesnikov, L. Beyer, X. Zhai, J. Puigcerver, J. Yung, S. Gelly, and N. Houlsby, “Big transfer (bit): General visual representation learning,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part V 16 . Springer, 2020, pp. 491–507
2020
Cited alongside, same era.
2022
Later among the works it cites.
Y. Wang, H. Chen, Y. Fan, W. Sun, R. Tao, W. Hou, R. Wang, L. Yang, Z. Zhou, L.-Z. Guo, H. Qi, Z. Wu, Y.-F. Li, S. Nakamura, W. Ye, M. Savvides, B. Raj, T. Shinozaki, B. Schiele, J. Wang, X. Xie, and Y. Zhang, “Usb: A unified semi-supervised learning benchmark,” in Advances in Neural Information Processing Systems (NeurIPS) , 2022
2022
Later among the works it cites.
H. Song, M. Kim, D. Park, Y. Shin, and J.-G. Lee, “Learning from noisy labels with deep neural networks: A survey,” IEEE Transactions on Neural Networks and Learning Systems , p. 1–19, 2022
2022
Later among the works it cites.
S. Li, X. Xia, S. Ge, and T. Liu, “Selective-supervised contrastive learning with noisy labels,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 316–325
2022
Later among the works it cites.
T. Nguyen, G. Ilharco, M. Wortsman, S. Oh, and L. Schmidt, “Quality not quantity: On the interaction between dataset design and robustness of clip,” Advances in Neural Information Processing Systems , vol. 35, pp. 21 455–21 469, 2022
2022
Later among the works it cites.
K. Lee, D. Ippolito, A. Nystrom, C. Zhang, D. Eck, C. Callison-Burch, and N. Carlini, “Deduplicating training data makes language models better,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL) , 2022
2022
Later among the works it cites.
N. Carlini, D. Ippolito, M. Jagielski, K. Lee, F. Tramer, and C. Zhang, “Quantifying memorization across neural language models,” in The Eleventh International Conference on Learning Representations , 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
S. Liu, Z. Zhu, Q. Qu, and C. You, “Robust training under label noise by over-parameterization,” in Proceedings of the International Conference on Machine Learning (ICML) , K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, and S. Sabato, Eds., vol. 162. PMLR, 17–23 Jul 2022, pp. 14 153–14 172
2022
Later among the works it cites.
J. Wei, Z. Zhu, H. Cheng, T. Liu, G. Niu, and Y. Liu, “Learning with noisy labels revisited: A study using real-world human annotations,” in International Conference on Learning Representations (ICLR) , 2022
2022
Later among the works it cites.
K. Wen, J. Teng, and J. Zhang, “Benign overfitting in classification: Provably counter label noise with larger models,” in The Eleventh International Conference on Learning Representations , 2022
2022
Later among the works it cites.
Y. Xue, K. Whitecross, and B. Mirzasoleiman, “Investigating why contrastive learning benefits robustness against label noise,” in International Conference on Machine Learning . PMLR, 2022, pp. 24 851–24 871
2022
Later among the works it cites.
J. Wei, H. Liu, T. Liu, G. Niu, M. Sugiyama, and Y. Liu, “To smooth or not? when label smoothing meets noisy labels,” in International Conference on Machine Learning (ICML) , 2022
2022
Later among the works it cites.
I. Magar and R. Schwartz, “Data contamination: From memorization to exploitation,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) , 2022, pp. 157–165
2022
Later among the works it cites.
M. Wortsman, G. Ilharco, J. W. Kim, M. Li, S. Kornblith, R. Roelofs, R. G. Lopes, H. Hajishirzi, A. Farhadi, H. Namkoong et al. , “Robust fine-tuning of zero-shot models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 7959–7971
2022
Later among the works it cites.
X. Liu, K. Ji, Y. Fu, W. Tam, Z. Du, Z. Yang, and J. Tang, “P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) , 2022, pp. 61–68
2022
Later among the works it cites.
H. Touvron, M. Cord, and H. Jégou, “Deit iii: Revenge of the vit,” in European Conference on Computer Vision . Springer, 2022, pp. 516–533
2022
Later among the works it cites.
S. Mangrulkar, S. Gugger, L. Debut, Y. Belkada, S. Paul, and B. Bossan, “Peft: State-of-the-art parameter-efficient fine-tuning methods,” https://github.com/huggingface/peft , 2022
2022
Later among the works it cites.
C. Wu, F. Wu, T. Qi, and Y. Huang, “Noisytune: A little noise can help you finetune pretrained language models better,” Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) , 2022
2022
Later among the works it cites.
A. Bardes, J. Ponce, and Y. LeCun, “Vicreg: Variance-invariance-covariance regularization for self-supervised learning,” in International Conference on Learning Representations (ICLR) , 2022
2022
Later among the works it cites.
T. Computer, “Redpajama: an open dataset for training large language models,” 2023. [Online]. Available: https://github.com/togethercomputer/RedPajama-Data
2023
Later among the works it cites.
OpenAI, “Gpt-4 technical report,” 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Zhang, B. Kang, B. Hooi, S. Yan, and J. Feng, “Deep long-tailed learning: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
Later among the works it cites.
Y. Wang, Z. Yu, J. Wang, Q. Heng, H. Chen, W. Ye, R. Xie, X. Xie, and S. Zhang, “Exploring vision-language models for imbalanced learning,” International Journal of Computer Vision (IJCV) , 2023
2023
Later among the works it cites.
S. Y. Gadre, G. Ilharco, A. Fang, J. Hayase, G. Smyrnis, T. Nguyen, R. Marten, M. Wortsman, D. Ghosh, J. Zhang, E. Orgad, R. Entezari, G. Daras, S. Pratt, V. Ramanujan, Y. Bitton, K. Marathe, S. Mussmann, R. Vencu, M. Cherti, R. Krishna, P. W. Koh, O. Saukh, A. J. Ratner, S. Song, H. Hajishirzi, A. Farhadi, R. Beaumont, S. Oh, A. G. Dimakis, J. Jitsev, Y. Carmon, V. Shankar, and L. Schmidt, “Datacomp: In search of the next generation of multimodal datasets,” ArXiv , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
D. Thiel, “Identifying and eliminating csam in generative ml training data and models,” Technical report, Stanford University, Palo Alto, CA, 2023, Tech. Rep., 2023. [Online]. Available: https://doi.org/10.25740/kh752sm9123
2023
Later among the works it cites.
M. Cherti, R. Beaumont, R. Wightman, M. Wortsman, G. Ilharco, C. Gordon, C. Schuhmann, L. Schmidt, and J. Jitsev, “Reproducible scaling laws for contrastive language-image learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 2818–2829
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
H. Cheng, Z. Zhu, X. Sun, and Y. Liu, “Mitigating memorization of noisy labels via regularization between representations,” in International Conference on Learning Representations (ICLR) , 2023
2023
Later among the works it cites.
Y. Wang, H. Chen, Q. Heng, W. Hou, Y. Fan, Z. Wu, J. Wang, M. Savvides, T. Shinozaki, B. Raj, B. Schiele, and X. Xie, “Freematch: Self-adaptive thresholding for semi-supervised learning,” in International Conference on Learning Representations (ICLR) , 2023
2023
Later among the works it cites.
Y. Wang, B. Zhang, W. Hou, Z. Wu, J. Wang, and T. Shinozaki, “Margin calibration for long-tailed visual recognition,” in Asian Conference on Machine Learning . PMLR, 2023, pp. 1101–1116
2023
Later among the works it cites.
S. Goyal, A. Kumar, S. Garg, Z. Kolter, and A. Raghunathan, “Finetune like you pretrain: Improved finetuning of zero-shot vision models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 19 338–19 347
2023
Later among the works it cites.
2023
Later among the works it cites.
C. Oh, H. Hwang, H.-y. Lee, Y. Lim, G. Jung, J. Jung, H. Choi, and K. Song, “Blackvip: Black-box visual prompting for robust transfer learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 24 224–24 235
2023
Later among the works it cites.
N. Kandpal, H. Deng, A. Roberts, E. Wallace, and C. Raffel, “Large language models struggle to learn long-tail knowledge,” in International Conference on Machine Learning . PMLR, 2023, pp. 15 696–15 707
2023
Later among the works it cites.
2023
Later among the works it cites.
J. A. Omiye, J. C. Lester, S. Spichak, V. Rotemberg, and R. Daneshjou, “Large language models propagate race-based medicine,” NPJ Digital Medicine , vol. 6, no. 1, p. 195, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
L. Yang, S. Zhang, L. Qin, Y. Li, Y. Wang, H. Liu, J. Wang, X. Xie, and Y. Zhang, “Glue-x: Evaluating natural language understanding models from an out-of-distribution generalization perspective,” in Findings of ACL , 2023
2023
Later among the works it cites.