SlowGuess/NeurIPS_2023_OCR huggingface dataset:
[68] Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, and Bo Han. Watermarking for out-of-distribution detection. In NeurIPS, 2022.
[69] Qizhou Wang, Zhen Fang, Yonggang Zhang, Feng Liu, Yixuan Li, and Bo Han. Learning to augment distributions for out-of-distribution detection. In NeurIPS, 2023.
[70] Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, and Bo Han. Out-of-distribution detection with implicit data transformation. In ICLR, 2023.
[71] Hongxin Wei, Renchunzi Xie, Hao Cheng, Lei Feng, Bo An, and Yixuan Li. Mitigating neural network overconfidence with logit normalization. In ICML, 2022.
[72] Xiaobo Xia, Wenhao Yang, Jie Ren, Yewen Li, Yibing Zhan, Bo Han, and Tongliang Liu. Pluralistic image completion with gaussian mixture models. In NeurIPS, 2022.
[73] Xiaobo Xia, Jiale Liu, Jun Yu, Xu Shen, Bo Han, and Tongliang Liu. Moderate coreset: A universal method of data selection for real-world data-efficient deep learning. In ICLR, 2023.
[74] Pingmei Xu, Krista A Ehinger, Yinda Zhang, Adam Finkelstein, Sanjeev R Kulkarni, and Jianxiong Xiao. Turkergaze: Crowdsourcing saliency with webcam based eye tracking. arXiv preprint arXiv:1504.06755, 2015.
[75] Jingkang Yang, Haoqi Wang, Litong Feng, Xiaopeng Yan, Huabin Zheng, Wayne Zhang, and Ziwei Liu. Semantically coherent out-of-distribution detection. In ICCV, 2021.
[76] Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao. Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365, 2015.
[77] Alireza Zaeemzadeh, Niccolò Bisagno, Zeno Sambugaro, Nicola Concí, Nazanin Rahnavard, and Mubarak Shah. Out-of-distribution detection using union of 1-dimensional subspaces. In CVPR, 2021.
[78] Sergey Zagoruyko and Nikos Komodakis. Wide residual networks. In BMVC, 2016.
[79] Jingyang Zhang, Nathan Inkawich, Randolph Linderman, Yiran Chen, and Hai Li. Mixture outlier exposure: Towards out-of-distribution detection in fine-grained environments. In WACV, 2023.
[80] Jingyang Zhang, Nathan Inkawich, Randolph Linderman, Ryan Luley, Yiran Chen, and Hai Li. Sio: Synthetic in-distribution data benefits out-of-distribution detection. arXiv preprint arXiv:2303.14531, 2023.
[81] Yongqi Zhang, Zhanke Zhou, Quanming Yao, and Yong Li. Efficient hyper-parameter search for knowledge graph embedding. In ACL, 2022.
[82] Yongqi Zhang, Zhanke Zhou, Quanming Yao, Xiaowen Chu, and Bo Han. Adaprop: Learning adaptive propagation for graph neural network based knowledge graph reasoning. In KDD, 2023.
[83] Xingjian Zhen, Zihang Meng, Rudrasis Chakraborty, and Vikas Singh. On the versatile uses of partial distance correlation in deep learning. In ECCV, 2022.
[84] Bolei Zhou, Ágata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba. Places: A 10 million image database for scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6):1452-1464, 2018.
[85] Zhanke Zhou, Jiangchao Yao, Jiaxu Liu, Xiawei Guo, Quanming Yao, Li He, Liang Wang, Bo Zheng, and Bo Han. Combating bilateral edge noise for robust link prediction. In NeurIPS, 2023.