SlowGuess/NeurIPS_2023_OCR huggingface dataset:
[57] A. G. Wilson. The use of entropy maximising models, in the theory of trip distribution, mode split and route split. Journal of transport economics and policy, pages 108-126, 1969.
[58] N. Yang, K. Zeng, Q. Wu, X. Jia, and J. Yan. Learning substructure invariance for out-of-distribution molecular representations. In Advances in Neural Information Processing Systems, 2022.
[59] N. Yang, K. Zeng, Q. Wu, and J. Yan. Molerec: Combinatorial drug recommendation with substructure-aware molecular representation learning. In Proceedings of the ACM Web Conference 2023, pages 4075-4085, 2023.
[60] S. Zhang, Z. Li, S. Yan, X. He, and J. Sun. Distribution alignment: A unified framework for long-tail visual recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2361-2370, 2021.
[61] X. Zhang, F. X. Yu, S. Karaman, W. Zhang, and S.-F. Chang. Heated-up softmax embedding. arXiv preprint arXiv:1809.04157, 2018.
[62] Y. Zhang, B. Hooi, L. Hong, and J. Feng. Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition. arXiv preprint arXiv:2107.09249, 2021.
[63] Y. Zhang, B. Hooi, D. Hu, J. Liang, and J. Feng. Unleashing the power of contrastive self-supervised visual models via contrast-regularized fine-tuning. Advances in Neural Information Processing Systems, 34:29848–29860, 2021.
[64] 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.
[65] B. Zhou, Q. Cui, X.-S. Wei, and Z.-M. Chen. Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9719–9728, 2020.