[81] J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel. The german traffic sign recognition benchmark: a multi-class classification competition. In *The 2011 international joint conference on neural networks*, pages 1453–1460. IEEE, 2011.
[82] J. Tack, J. Park, H. Lee, J. Lee, and J. Shin. Meta-learning with self-improving momentum target. In Advances in Neural Information Processing Systems, 2022.
[83] A. Tamkin, V. Liu, R. Lu, D. Fein, C. Schultz, and N. D. Goodman. Dabs: a domain-agnostic benchmark for self-supervised learning. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks, 2021.
[84] A. Tamkin, M. Wu, and N. D. Goodman. Viewmaker networks: Learning views for unsupervised representation learning. In International Conference on Learning Representations, 2021.
[85] A. Tamkin, G. Banerjee, M. Owda, V. Liu, S. Rammoorthy, and N. D. Goodman. Dabs 2.0: Improved datasets and algorithms for universal self-supervision. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS, 2022.
[86] S. Thrun and L. Pratt. Learning to Learn. Springer, 1998.
[87] M. K. Titsias, J. Schwarz, A. G. d. G. Matthews, R. Pascanu, and Y. W. Teh. Functional regularisation for continual learning with gaussian processes. In International Conference on Learning Representations, 2020.
[88] A. van den Oord, Y. Li, and O. Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018.
[89] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems, 2017.
[90] V. Verma, T. Luong, K. Kawaguchi, H. Pham, and Q. V. Le. Towards domain-agnostic contrastive learning. In International Conference on Machine Learning, 2021.
[91] P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol. Extracting and composing robust features with denoising autoencoders. In International Conference on Machine Learning, pages 1096-1103, 2008.
[92] O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra, et al. Matching networks for one shot learning. In Advances in Neural Information Processing Systems, 2016.
[93] C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie. The caltech-ucsd birds-200-2011 dataset. Technical Report CNS-TR-2010-001, 2010.
[94] H. Wang, K. Song, J. Fan, Y. Wang, J. Xie, and Z. Zhang. Hard patches mining for masked image modeling. In IEEE Conference on Computer Vision and Pattern Recognition, 2023.
[95] W. Wang, H. Bao, L. Dong, J. Bjorck, Z. Peng, Q. Liu, K. Aggarwal, O. K. Mohammed, S. Singhal, S. Som, and F. Wei. Image as a foreign language: Beit pretraining for all vision and vision-language tasks. arXiv preprint arXiv:2208.10442, 2022.
[96] P. Warden. Speech commands: A dataset for limited-vocabulary speech recognition. arXiv preprint arXiv:1804.03209, 2018.
[97] C. Wei, H. Fan, S. Xie, C.-Y. Wu, A. L. Yuille, and C. Feichtenhofer. Masked feature prediction for self-supervised visual pre-training. In IEEE Conference on Computer Vision and Pattern Recognition, 2022.
[98] M.-J. Wu, J.-S. R. Jang, and J.-L. Chen. Wafer map failure pattern recognition and similarity ranking for large-scale data sets. IEEE Transactions on Semiconductor Manufacturing, 28(1): 1-12, 2014.