SlowGuess/NeurIPS_2023_OCR huggingface dataset:
[29] Chenghao Liu, Zhihao Wang, Doyen Sahoo, Yuan Fang, Kun Zhang, and Steven CH Hoi. Adaptive task sampling for meta-learning. In ECCV, 2020.
[30] David A McAllester. Some PAC-Bayesian theorems. Machine Learning, 37:355-363, 1998.
[31] David A McAllester. PAC-Bayesian model averaging. In  $COLT$ , 1999.
[32] Timo Milbich, Karsten Roth, Samarth Sinha, Ludwig Schmidt, Marzyeh Ghassemi, and Bjorn Ommer. Characterizing generalization under out-of-distribution shifts in deep metric learning. In NeurIPS, 2021.
[33] Jaehoon Oh, Sungnyun Kim, Namgyu Ho, Jin-Hwa Kim, Hwanjun Song, and Se-Young Yun. Understanding cross-domain few-shot learning based on domain similarity and few-shot difficulty. In NeurIPS, 2022.
[34] Genevieve Patterson, Chen Xu, Hang Su, and James Hays. The sun attribute database: Beyond categories for deeper scene understanding. International Journal of Computer Vision, 108:59-81, 2014.
[35] Anastasia Pentina and Christoph Lampert. A PAC-Bayesian bound for lifelong learning. In ICML, 2014.
[36] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021.
[37] Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang, James Lucas, Jake Snell, Xaq Pitkow, Andreas S Tolias, and Richard Zemel. Probing few-shot generalization with attributes. arXiv, 2020.
[38] Jonas Rothfuss, Vincent Fortuin, Martin Josifoski, and Andreas Krause. PACOH: Bayes-optimal meta-learning with PAC-guarantees. In ICML, 2021.
[39] Yossi Rubner, Carlo Tomasi, and Leonidas J Guibas. A metric for distributions with applications to image databases. In ICCV, 1998.
[40] Mert Bulent Sariyildiz, Yannis Kalantidis, Diane Larlus, and Karteek Alahari. Concept generalization in visual representation learning. In ICCV, 2021.
[41] Abhinav Shrivastava, Abhinav Gupta, and Ross Girshick. Training region-based object detectors with online hard example mining. In CVPR, 2016.
[42] Jake Snell, Kevin Swersky, and Richard Zemel. Prototypical networks for few-shot learning. In NeurIPS, 2017.
[43] Qianru Sun, Yaoyao Liu, Zhaozheng Chen, Tat-Seng Chua, and Bernt Schiele. Meta-transfer learning through hard tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44:1443-1456, 2020.
[44] Qianru Sun, Yaoyao Liu, Tat-Seng Chua, and Bernt Schiele. Meta-transfer learning for few-shot learning. In CVPR, 2019.
[45] Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales. Learning to compare: Relation network for few-shot learning. In CVPR, 2018.
[46] Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, et al. Meta-dataset: A dataset of datasets for learning to learn from few examples. In ICLR, 2020.
[47] Nilesh Tripuraneni, Chi Jin, and Michael Jordan. Provable meta-learning of linear representations. In ICML, 2021.
[48] Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al. Matching networks for one shot learning. In NeurIPS, 2016.
[49] Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. The caltech-ucsd birds-200-2011 dataset. 2011.
[50] Jiangtao Xie, Fei Long, Jiaming Lv, Qilong Wang, and Peihua Li. Joint distribution matters: Deep brownian distance covariance for few-shot classification. In CVPR, 2022.