[22] P. W. Koh and P. Liang. Understanding black-box predictions via influence functions. In ICML, 2017.
[23] P. W. Koh, S. Sagawa, H. Marklund, S. M. Xie, M. Zhang, A. Balsubramani, W. Hu, M. Yasunaga, R. L. Phillips, I. Gao, et al. Wilds: A benchmark of in-the-wild distribution shifts. In ICML, 2021.
[24] J. Kuan and J. Mueller. Model-agnostic label quality scoring to detect real-world label errors. In ICML DataPerf Workshop, 2022.
[25] S. Laine and T. Aila. Temporal ensembling for semi-supervised learning. In ICLR, 2017.
[26] K. Lee, K. Lee, H. Lee, and J. Shin. A simple unified framework for detecting out-of-distribution samples and adversarial attacks. In NeurIPS, 2018.
[27] J. Li, R. Socher, and S. C. Hoi. Dividemix: Learning with noisy labels as semi-supervised learning. In ICLR, 2020.
[28] S. Liang, Y. Li, and R. Srikant. Enhancing the reliability of out-of-distribution image detection in neural networks. In ICLR, 2018.
[29] W. Liu, X. Wang, J. Owens, and Y. Li. Energy-based out-of-distribution detection. In NeurIPS, 2020.
[30] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692, 2019.
[31] Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie. A convnet for the 2020s. In CVPR, 2022.
[32] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas. Communication-efficient learning of deep networks from decentralized data. In AISTATS, 2017.
[33] C. Northcutt, L. Jiang, and I. Chuang. Confident learning: Estimating uncertainty in dataset labels. Journal of Artificial Intelligence Research, 70, 2021.
[34] C. G. Northcutt, A. Athalye, and J. Mueller. Pervasive label errors in test sets destabilize machine learning benchmarks. In NeurIPS Datasets and Benchmarks Track, 2021.
[35] G. I. Parisi, R. Kemker, J. L. Part, C. Kanan, and S. Wermter. Continual lifelong learning with neural networks: A review. Neural Networks, 113, 2019.
[36] W. Park, D. Kim, Y. Lu, and M. Cho. Relational knowledge distillation. In CVPR, 2019.
[37] K. J. Piczak. ESC: Dataset for Environmental Sound Classification. In Proceedings of the 23rd Annual ACM Conference on Multimedia, 2015.
[38] G. Pleiss, T. Zhang, E. Elenberg, and K. Q. Weinberger. Identifying mislabeled data using the area under the margin ranking. In NeurIPS, 2020.
[39] G. Pruthi, F. Liu, S. Kale, and M. Sundararajan. Estimating training data influence by tracing gradient descent. In NeurIPS, 2020.
[40] A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 2022.
[41] S. Reed, H. Lee, D. Anguelov, C. Szegedy, D. Erhan, and A. Rabinovich. Training deep neural networks on noisy labels with bootstrapping. arXiv preprint arXiv:1412.6596, 2014.
[42] N. Reimers and I. Gurevych. Sentence-bert: Sentence embeddings using siamese bert-networks. In EMNLP-IJCNLP, 2019.
[43] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3), 2015.
[44] F. Schroff, D. Kalenichenko, and J. Philbin. Facenet: A unified embedding for face recognition and clustering. In CVPR, 2015.
[45] K. Shuster, J. Xu, M. Komeili, D. Ju, E. M. Smith, S. Roller, M. Ung, M. Chen, K. Arora, J. Lane, et al. Blenderbot 3: a deployed conversational agent that continually learns to responsibly engage. arXiv preprint arXiv:2208.03188, 2022.