{"type":"ScholarlyArticle","title":"Boosting Model Resilience via Implicit Adversarial Data Augmentation","authors":[{"name":"Xiaoling Zhou","slug":"xiaoling-zhou","given":"Xiaoling","family":"Zhou","affiliation":"National Engineering Research Center for Software Engineering , Peking University , China"},{"name":"Wei Ye","slug":"wei-ye","given":"Wei","family":"Ye","affiliation":"National Engineering Research Center for Software Engineering , Peking University , China"},{"name":"Zhemg Lee","slug":"zhemg-lee","given":"Zhemg","family":"Lee","affiliation":"Tianjin University , Tianjin , China"},{"name":"Rui Xie","slug":"rui-xie","given":"Rui","family":"Xie","affiliation":"National Engineering Research Center for Software Engineering , Peking University , China"},{"name":"Shikun Zhang","slug":"shikun-zhang","given":"Shikun","family":"Zhang","affiliation":"National Engineering Research Center for Software Engineering , Peking University , China"}],"abstract":"Data augmentation plays a pivotal role in enhancing and diversifying training data. Nonetheless, consistently improving model performance in varied learning scenarios, especially those with inherent data biases, remains challenging. To address this, we propose to augment the deep features of samples by incorporating their adversarial and anti-adversarial perturbation distributions, enabling adaptive adjustment in the learning difficulty tailored to each sample’s specific characteristics. We then theoretically reveal that our augmentation process approximates the optimization of a surrogate loss function as the number of augmented copies increases indefinitely. This insight leads us to develop a meta-learning-based framework for optimizing classifiers with this novel loss, introducing the effects of augmentation while bypassing the explicit augmentation process. We conduct extensive experiments across four common biased learning scenarios: long-tail learning, generalized long-tail learning, noisy label learning, and subpopulation shift learning. The empirical results demonstrate that our method consistently achieves state-of-the-art performance, highlighting its broad adaptability.","doi":"10.24963/ijcai.2024/625","url":"https://ijcai.org/proceedings/2024/625","pdf":"https://ijcai.org/proceedings/2024/0625.pdf","markdown":"https://ijcai.org/proceedings/2024/625.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/625","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":625,"pages":{"first":5653,"last":5661},"keywords":["Machine Learning: ML: Classification","Data Mining: DM: Class imbalance and unequal cost","Machine Learning: ML: Robustness","Machine Learning: ML: Meta-learning"],"videos":[],"references":[{"position":1,"raw":"[Ahuja et al., 2021] Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Yoshua Bengio, Ioannis Mitliagkas, and Irina Rish. Invariance principle meets information bottleneck for out-of-distribution generalization. In NeurIPS, pages 3438-3450, 2021.","title":"Invariance principle meets information bottleneck for out-of-distribution generalization","authors":"Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Yoshua Bengio, Ioannis Mitliagkas, Irina Rish","year":"2021","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Arjovsky et al., 2019] Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. Invariant risk minimization. arXiv preprint arXiv:1907.02893, 2019.","title":"Invariant risk minimization","authors":"Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, David Lopez-Paz","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Borkan et al., 2019] Daniel Borkan, Lucas Dixon, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman. Nuanced metrics for measuring unintended bias with real data for text classification. In WWW, pages 491-500, 2019.","title":"Nuanced metrics for measuring unintended bias with real data for text classification","authors":"Daniel Borkan, Lucas Dixon, Jeffrey Sorensen, Nithum Thain, Lucy Vasserman","year":"2019","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cao et al., 2019] Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma. Learning imbalanced datasets with label-distribution-aware margin loss. In NeurIPS, pages 1567-1578, 2019.","title":"Learning imbalanced datasets with label-distribution-aware margin loss","authors":"Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, Tengyu Ma","year":"2019","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen et al., 2022] Xiaohua Chen, Yucan Zhou, Dayan Wu, Wanqian Zhang, Yu Zhou, Bo Li, and Weiping Wang. Imagine by reasoning: A reasoning-based implicit seman- tic data augmentation for long-tailed classification. In AAAI, pages 356-364, 2022.","title":"Imagine by reasoning: A reasoning-based implicit semantic data augmentation for long-tailed classification","authors":"Xiaohua Chen, Yucan Zhou, Dayan Wu, Wanqian Zhang, Yu Zhou, Bo Li, Weiping Wang","year":"2022","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cubuk et al., 2020] Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V. Le. Randaugment: Practical auto- mated data augmentation with a reduced search space. In CVPR Workshops, pages 3008-3017, 2020.","title":"Randaugment: Practical automated data augmentation with a reduced search space","authors":"D Ekin, Barret Cubuk, Jonathon Zoph, Shlens, V Quoc, Le","year":"2020","venue":"CVPR Workshops","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Cui et al., 2019] Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie. Class-balanced loss based on effective number of samples. In CVPR, pages 9268-9277, 2019.","title":"Class-balanced loss based on effective number of samples","authors":"Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, Serge Belongie","year":"2019","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Han et al., 2018] Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, and Masashi Sugiyama. Co-teaching: Robust training of deep neural networks with extremely noisy labels. In NeurIPS, pages 8536-8546, 2018.","title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels","authors":"Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W Tsang, Masashi Sugiyama","year":"2018","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In CVPR, pages 770-778, 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Hendrycks et al., 2018] Dan Hendrycks, Mantas Mazeika, Duncan Wilson, and Kevin Gimpel. Using trusted data to train deep networks on labels corrupted by severe noise. In NeurIPS, pages 10477-10486, 2018.","title":"Using trusted data to train deep networks on labels corrupted by severe noise","authors":"Dan Hendrycks, Mantas Mazeika, Duncan Wilson, Kevin Gimpel","year":"2018","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Hong et al., 2021] Youngkyu Hong, Seungju Han, Kwanghee Choi, Seokjun Seo, Beomsu Kim, and Buru Chang. Disentangling label distribution for long- tailed visual recognition. In CVPR, pages 6626-6636, 2021.","title":"Disentangling label distribution for longtailed visual recognition","authors":"Hong","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hong et al., 2022] Yan Hong, Jianfu Zhang, Zhongyi Sun, and Ke Yan. Safa: Sample-adaptive feature augmentation for long-tailed image classification. In ECCV, pages 587- 603, 2022.","title":"Safa: Sample-adaptive feature augmentation for long-tailed image classification","authors":"Yan Hong, Jianfu Zhang, Zhongyi Sun, Ke Yan","year":"2022","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Horn et al., 2018] Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie. The inaturalist species classification and detection dataset. In CVPR, pages 8769- 8778, 2018.","title":"The inaturalist species classification and detection dataset","authors":"Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, Serge Belongie","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Jamal et al., 2020] Muhammad Abdullah Jamal, Matthew Brown, Ming-Hsuan Yang, Liqiang Wang, and Boqing Gong. Rethinking class-balanced methods for long-tailed visual recognition from a domain adaptation perspective. In CVPR, pages 7610-7619, 2020.","title":"Rethinking class-balanced methods for long-tailed visual recognition from a domain adaptation perspective","authors":"Muhammad Abdullah, Jamal, Matthew Brown, Ming-Hsuan Yang, Liqiang Wang, Boqing Gong","year":"2020","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Jiang et al., 2018] Lu Jiang, Zhengyuan Zhou, Thomas Le- ung, Li-Jia Li, and Fei-Fei Li. Mentornet: Learning data- driven curriculum for very deep neural networks on cor- rupted labels. In ICML, pages 2304-2313, 2018.","title":"Mentornet: Learning datadriven curriculum for very deep neural networks on corrupted labels","authors":"Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, Fei-Fei Li","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Kang et al., 2020] Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis. Decoupling representation and classi- fier for long-tailed recognition. In ICLR, 2020.","title":"Decoupling representation and classifier for long-tailed recognition","authors":"Kang","year":"2020","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Krizhevsky and Hinton, 2009] Alex Krizhevsky and Geof- frey Hinton. Learning multiple layers of features from tiny images. Technical report, 2009.","title":"Learning multiple layers of features from tiny images","authors":"Alex Krizhevsky, Geoffrey Hinton","year":"2009","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Krueger et al., 2021] David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, and Aaron Courville. Out-of-distribution generalization via risk extrapolation (rex). In ICML, pages 5815-5826, 2021.","title":"Out-of-distribution generalization via risk extrapolation (rex)","authors":"David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, Aaron Courville","year":"2021","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Lee et al., 2023] Sungyoon Lee, Hoki Kim, and Jaewook Lee. Graddiv: Adversarial robustness of randomized neu- ral networks via gradient diversity regularization. IEEE TPAMI, 45(2):2645-2651, 2023.","title":"Graddiv: Adversarial robustness of randomized neural networks via gradient diversity regularization","authors":"Lee","year":"2023","venue":"IEEE TPAMI","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Li et al., 2018] Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C. Kot. Domain generalization with adversarial feature learning. In CVPR, pages 5400-5409, 2018.","title":"Domain generalization with adversarial feature learning","authors":"Haoliang Li, Sinno Jialin Pan, Shiqi Wang, Alex C Kot","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Li et al., 2021] Shuang Li, Kaixiong Gong, Chi-Harold Liu, Yulin Wang, Feng Qiao, and Xinjing Cheng. Metasaug: Meta semantic augmentation for long-tailed visual recog- nition. In CVPR, pages 5208-5217, 2021.","title":"Metasaug: Meta semantic augmentation for long-tailed visual recognition","authors":"Shuang Li, Kaixiong Gong, Chi-Harold Liu, Yulin Wang, Feng Qiao, Xinjing Cheng","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Li et al., 2022] Mengyang Li, Fengguang Su, Ou Wu, et al. Logit perturbation. In AAAI, pages 1359-1366, 2022.","title":"Logit perturbation","authors":"Li","year":"2022","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Liu et al., 2016] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. Deep learning face attributes in the wild. In ICCV, pages 3730-3738, 2016.","title":"Deep learning face attributes in the wild","authors":"Ziwei Liu, Ping Luo, Xiaogang Wang, Xiaoou Tang","year":"2016","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Liu et al., 2019] Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, and Stella X. Yu. Large-scale long-tailed recognition in an open world. In CVPR, pages 2537-2546, 2019.","title":"Large-scale long-tailed recognition in an open world","authors":"Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, Stella X Yu","year":"2019","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Ma et al., 2018] Xingjun Ma, Yisen Wang, Michael E. Houle, Shuo Zhou, Sarah Erfani, Shutao Xia, Sudanthi Wijewickrema, et al. Dimensionality-driven learning with noisy labels. In ICML, pages 3355-3364, 2018.","title":"Dimensionality-driven learning with noisy labels","authors":"Ma","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Ma et al., 2020] Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano, Sarah Erfani, and James Bailey. Nor- malized loss functions for deep learning with noisy labels. In ICML, pages 6543-6553, 2020.","title":"Normalized loss functions for deep learning with noisy labels","authors":"Ma","year":"2020","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Madry et al., 2018] Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning models resistant to adversarial attacks. In ICLR, 2018.","title":"Towards deep learning models resistant to adversarial attacks","authors":"Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu","year":"2018","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Maharana et al., 2022] Kiran Maharana, Surajit Mondal, and Bhushankumar Nemade. A review: Data pre- processing and data augmentation techniques. Global Transitions Proceedings, 3(1):91-99, 2022.","title":"A review: Data preprocessing and data augmentation techniques","authors":"Kiran Maharana, Surajit Mondal, Bhushankumar Nemade","year":"2022","venue":"Global Transitions Proceedings","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Menon et al., 2021] Aditya Krishna Menon, Sadeep Jaya- sumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, and Sanjiv Kumar. Long-tail learning via logit ad- justment. In ICLR, 2021.","title":"Long-tail learning via logit adjustment","authors":"Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, Sanjiv Kumar","year":"2021","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Ren et al., 2018] Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun. Learning to reweight examples for robust deep learning. In ICML, pages 4334-4343, 2018.","title":"Learning to reweight examples for robust deep learning","authors":"Ren","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Ren et al., 2020] Jiawei Ren, Cunjun Yu, Shunan Sheng, Xiao Ma, Haiyu Zhao, Shuai Yi, and Hongsheng Li. Bal- anced meta-softmax for long-tailed visual recognition. In NeurIPS, pages 4175-4186, 2020.","title":"Balanced meta-softmax for long-tailed visual recognition","authors":"Cunjun Jiawei Ren, Shunan Yu, Xiao Sheng, Haiyu Ma, Shuai Zhao, Hongsheng Yi, Li","year":"2020","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Ricci et al., 2023] Simone Ricci, Tiberio Uricchio, and Al- berto Del Bimbo. Meta-learning advisor networks for long-tail and noisy labels in social image classification. ACM TOMM, 19(5s):1-23, 2023.","title":"Meta-learning advisor networks for long-tail and noisy labels in social image classification","authors":"Simone Ricci, Tiberio Uricchio, Alberto Del Bimbo","year":"2023","venue":"ACM TOMM","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Russakovsky et al., 2015] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, and Michael Bernstein. Imagenet large scale visual recogni- tion challenge. IJCV, 115(3):211-252, 2015.","title":"Imagenet large scale visual recognition challenge","authors":"Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein","year":"2015","venue":"IJCV","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Sagawa et al., 2020] Shiori Sagawa, Pang Wei Koh, Tat- sunori B Hashimoto, et al. Distributionally robust neural networks for group shifts: On the importance of regular- ization for worst-case generalization. In ICLR, 2020.","title":"Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization","authors":"Shiori Sagawa, Pang Wei Koh, B Tatsunori, Hashimoto","year":"2020","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Sanh et al., 2019] Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. Distilbert, a distilled ver- sion of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108, 2019.","title":"Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter","authors":"Victor Sanh, Lysandre Debut, Julien Chaumond, Thomas Wolf","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Shi et al., 2022] Yuge Shi, Jeffrey Seely, Philip H.S. Torr, N. Siddharth, Awni Hannun, Nicolas Usunier, and Gabriel Synnaeve. Gradient matching for domain generalization. In ICLR, 2022.","title":"Gradient matching for domain generalization","authors":"Yuge Shi, Jeffrey Seely, H S Philip, N Torr, Awni Siddharth, Nicolas Hannun, Gabriel Usunier, Synnaeve","year":"2022","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Shu et al., 2019] Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, and Deyu Meng. Meta- weight-net: Learning an explicit mapping for sample weighting. In NeurIPS, pages 1919-1930, 2019.","title":"Metaweight-net: Learning an explicit mapping for sample weighting","authors":"Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, Deyu Meng","year":"2019","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Tang et al., 2020] Kaihua Tang, Jianqiang Huang, and Han- wang Zhang. Long-tailed classification by keeping the good and removing the bad momentum causal effect. In NeurIPS, pages 1513-1524, 2020.","title":"Long-tailed classification by keeping the good and removing the bad momentum causal effect","authors":"Kaihua Tang, Jianqiang Huang, Hanwang Zhang","year":"2020","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Tang et al., 2022] Kaihua Tang, Mingyuan Tao, Jiaxin Qi, Zhenguang Liu, and Hanwang Zhang. Invariant fea- ture learning for generalized long-tailed classification. In ECCV, pages 709-726, 2022.","title":"Invariant feature learning for generalized long-tailed classification","authors":"Kaihua Tang, Mingyuan Tao, Jiaxin Qi, Zhenguang Liu, Hanwang Zhang","year":"2022","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Taylor and Nitschke, 2018] Luke Taylor and Geoff Nitschke. Improving deep learning with generic data augmentation. In SSCI, pages 1542-1547, 2018.","title":"Improving deep learning with generic data augmentation","authors":"Nitschke Taylor, Luke Taylor, Geoff Nitschke","year":"2018","venue":"SSCI","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Wang et al., 2019] Yulin Wang, Xuran Pan, Shiji Song, Hong Zhang, Cheng Wu, and Gao Huang. Implicit se- mantic data augmentation for deep networks. In NeurIPS, pages 12635-12644, 2019.","title":"Implicit semantic data augmentation for deep networks","authors":"Yulin Wang, Xuran Pan, Shiji Song, Hong Zhang, Cheng Wu, Gao Huang","year":"2019","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Wei et al., 2020] Hongxin Wei, Lei Feng, Xiangyu Chen, and Bo An. Combating noisy labels by agreement: A joint training method with co-regularization. In CVPR, pages 13723-13732, 2020.","title":"Combating noisy labels by agreement: A joint training method with co-regularization","authors":"Hongxin Wei, Lei Feng, Xiangyu Chen, Bo An","year":"2020","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Xie et al., 2017] Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He. Aggregated residual trans- formations for deep neural networks. In CVPR, pages 5987-5995, 2017.","title":"Aggregated residual transformations for deep neural networks","authors":"Xie","year":"2017","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Xu and Zhao, 2023] Xiaogang Xu and Hengshuang Zhao. Universal adaptive data augmentation. In IJCAI, pages 1596-1603, 2023.","title":"Universal adaptive data augmentation","authors":"Zhao Xu, Xiaogang Xu, Hengshuang Zhao","year":"2023","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2023/177"},{"position":45,"raw":"[Xu et al., 2019] Yilun Xu, Peng Cao, Yuqing Kong, and Yizhou Wang. L dmi: A novel information-theoretic loss function for training deep nets robust to label noise. In NeurIPS, pages 6225-6236, 2019.","title":"L dmi: A novel information-theoretic loss function for training deep nets robust to label noise","authors":"Yilun Xu, Peng Cao, Yuqing Kong, Yizhou Wang","year":"2019","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Xu et al., 2020] Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, and Wenjun Zhang. Ad- versarial domain adaptation with domain mixup. In AAAI, pages 6502-6509, 2020.","title":"Adversarial domain adaptation with domain mixup","authors":"Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, Wenjun Zhang","year":"2020","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Xu et al., 2021] Han Xu, Xiaorui Liu, Yaxin Li, Anil Jain, and Jiliang Tang. To be robust or to be fair: Towards fair- ness in adversarial training. In ICML, pages 11492-11501, 2021.","title":"To be robust or to be fair: Towards fairness in adversarial training","authors":"Han Xu, Xiaorui Liu, Yaxin Li, Anil Jain, Jiliang Tang","year":"2021","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Yao et al., 2022] Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, and Chelsea Finn. Im- proving out-of-distribution robustness via selective aug- mentation. In ICML, pages 25407-25437, 2022.","title":"Improving out-of-distribution robustness via selective augmentation","authors":"Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, Chelsea Finn","year":"2022","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Zagoruyko and Komodakis, 2016] Sergey Zagoruyko and Nikos Komodakis. Wide residual networks. arXiv preprint arXiv:1605.07146, 2016.","title":"Wide residual networks","authors":"Sergey Zagoruyko, Nikos Komodakis","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Zhang et al., 2018] Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz. Mixup: Beyond empirical risk minimization. In ICLR, 2018.","title":"Mixup: Beyond empirical risk minimization","authors":"Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, David Lopez-Paz","year":"2018","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Zhang et al., 2023] Shaoyu Zhang, Chen Chen, Xiyuan Hu, and Silong Peng. Balanced knowledge distillation for long-tailed learning. Neurocomputing, 527:36-46, 2023.","title":"Balanced knowledge distillation for long-tailed learning","authors":"Zhang","year":"2023","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Zheng et al., 2021] Guoqing Zheng, Ahmed Hassan Awadallah, and Susan Dumais. Meta label correction for noisy label learning. In AAAI, pages 11053-11061, 2021.","title":"Meta label correction for noisy label learning","authors":"Guoqing Zheng, Ahmed Hassan Awadallah, Susan Dumais","year":"2021","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Zhong et al., 2021] Zhisheng Zhong, Jiequan Cui, Shu Liu, and Jiaya Jia. Improving calibration for long-tailed recog- nition. In CVPR, pages 16489-16498, 2021.","title":"Improving calibration for long-tailed recognition","authors":"Zhisheng Zhong, Jiequan Cui, Shu Liu, Jiaya Jia","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Zhou et al., 2020] Boyan Zhou, Quan Cui, Xiu-Shen Wei, and Zhao-Min Chen. Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition. In CVPR, pages 9719-9728, 2020.","title":"Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition","authors":"Boyan Zhou, Quan Cui, Xiu-Shen Wei, Zhao-Min Chen","year":"2020","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":55,"raw":"[Zhou et al., 2023] Xiaoling Zhou, Nan Yang, and Ou Wu. Combining adversaries with anti-adversaries in training. In AAAI, pages 11435-11442, 2023.","title":"Combining adversaries with anti-adversaries in training","authors":"Xiaoling Zhou, Nan Yang, Ou Wu","year":"2023","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":56,"raw":"[Zhu et al., 2021] Jianing Zhu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Hongxia Yang, Mohan Kankan- halli, and Masashi Sugiyama. Understanding the inter- action of adversarial training with noisy labels. arXiv preprint arXiv:2102.03482, 2021.","title":"Understanding the interaction of adversarial training with noisy labels","authors":"Jianing Zhu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Hongxia Yang, Mohan Kankanhalli, Masashi Sugiyama","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}