{"type":"ScholarlyArticle","title":"Boosting Single Positive Multi-label Classification with Generalized Robust Loss","authors":[{"name":"Yanxi Chen","slug":"yanxi-chen","given":"Yanxi","family":"Chen","affiliation":"University of International Business and Economics"},{"name":"Chunxiao Li","slug":"chunxiao-li","given":"Chunxiao","family":"Li","affiliation":"University of International Business and Economics"},{"name":"Xinyang Dai","slug":"xinyang-dai","given":"Xinyang","family":"Dai","affiliation":"University of International Business and Economics"},{"name":"Jinhuan Li","slug":"jinhuan-li","given":"Jinhuan","family":"Li","affiliation":"University of International Business and Economics"},{"name":"Weiyu Sun","slug":"weiyu-sun","given":"Weiyu","family":"Sun","affiliation":"University of International Business and Economics"},{"name":"Yiming Wang","slug":"yiming-wang","given":"Yiming","family":"Wang","affiliation":"University of International Business and Economics"},{"name":"Renyuan Zhang","slug":"renyuan-zhang","given":"Renyuan","family":"Zhang","affiliation":"University of International Business and Economics"},{"name":"Tinghe Zhang","slug":"tinghe-zhang","given":"Tinghe","family":"Zhang","affiliation":"University of International Business and Economics"},{"name":"Bo Wang","slug":"bo-wang","given":"Bo","family":"Wang","affiliation":"University of International Business and Economics"}],"abstract":"Multi-label learning (MLL) requires comprehensive multi-semantic annotations that is hard to fully obtain, thus often resulting in missing labels scenarios. In this paper, we investigate Single Positive Multi-label Learning (SPML), where each image is associated with merely one positive label. Existing SPML methods only focus on designing losses using mechanisms such as hard pseudo-labeling and robust losses, mostly leading to unacceptable false negatives. To address this issue, we first propose a generalized loss framework based on expected risk minimization to provide soft pseudo labels, and point out that the former losses can be seamlessly converted into our framework. In particular, we design a novel robust loss based on our framework, which enjoys flexible coordination between false positives and false negatives, and can additionally deal with the imbalance between positive and negative samples. Extensive experiments show that our approach can significantly improve SPML performance and outperform the vast majority of state-of-the-art methods on all the four benchmarks. Our code is available at https://github.com/yan4xi1/GRLoss.","doi":"10.24963/ijcai.2024/423","url":"https://ijcai.org/proceedings/2024/423","pdf":"https://ijcai.org/proceedings/2024/0423.pdf","markdown":"https://ijcai.org/proceedings/2024/423.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/423","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":423,"pages":{"first":3825,"last":3833},"keywords":["Machine Learning: ML: Weakly supervised learning","Machine Learning: ML: Classification","Machine Learning: ML: Multi-label learning"],"videos":[],"references":[{"position":1,"raw":"[Bekker and Davis, 2020] Jessa Bekker and Jesse Davis. Learning from positive and unlabeled data: A survey. Ma- chine Learning, 109:719-760, 2020.","title":"Learning from positive and unlabeled data: A survey","authors":"Davis ; Bekker, Jessa Bekker, Jesse Davis","year":"2020","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chen et al., 2023] Hao Chen, Ran Tao, Yue Fan, Yidong Wang, Jindong Wang, Bernt Schiele, Xing Xie, Bhiksha Raj, and Marios Savvides. SoftMatch: Addressing the quantity-quality tradeoff in semi-supervised learning. In ICLR, 2023.","title":"SoftMatch: Addressing the quantity-quality tradeoff in semi-supervised learning","authors":"Ran Hao Chen, Yue Tao, Yidong Fan, Jindong Wang, Bernt Wang, Xing Schiele, Bhiksha Xie, Marios Raj, Savvides","year":"2023","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chua et al., 2009] Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, and Yantao Zheng. Nus- wide: a real-world web image database from national uni- versity of singapore. In Proceedings of the ACM CIVR, pages 1-9, 2009.","title":"Nuswide: a real-world web image database from national university of singapore","authors":"Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, Yantao Zheng","year":"2009","venue":"Proceedings of the ACM CIVR","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cole et al., 2021] Elijah Cole, Oisin Mac Aodha, Titouan Lorieul, Pietro Perona, Dan Morris, and Nebojsa Jojic. Multi-label learning from single positive labels. In CVPR, pages 933-942, 2021.","title":"Multi-label learning from single positive labels","authors":"Cole","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Durand et al., 2019] Thibaut Durand, Nazanin Mehrasa, and Greg Mori. Learning a deep ConvNet for multi-label classification with partial labels. In CVPR, pages 647-657, 2019.","title":"Learning a deep ConvNet for multi-label classification with partial labels","authors":"Thibaut Durand, Nazanin Mehrasa, Greg Mori","year":"2019","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Everingham and Winn, 2012] Mark Everingham and John Winn. The PASCAL visual object classes challenge 2012 (VOC2012) development kit. Pattern Anal. Stat. Model. Comput. Learn., Tech. Rep, 2007(1-45):5, 2012.","title":"The PASCAL visual object classes challenge 2012 (VOC2012) development kit","authors":"Mark Everingham, John Winn","year":"2007","venue":"Pattern Anal. Stat. Model. Comput. Learn., Tech. Rep","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Feng et al., 2020] Lei Feng, Jun Huang, Senlin Shu, and Bo An. Regularized matrix factorization for multilabel learning with missing labels. IEEE Transactions on Cy- bernetics, 52(5):3710-3721, 2020.","title":"Regularized matrix factorization for multilabel learning with missing labels","authors":"Lei Feng, Jun Huang, Senlin Shu, Bo An","year":"2020","venue":"IEEE Transactions on Cybernetics","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Feng et al., 2021] Lei Feng, Senlin Shu, Zhuoyi Lin, Feng- mao Lv, Li Li, and Bo An. Can cross entropy loss be ro- bust to label noise? In Proceedings of the Twenty-Ninth In- ternational Conference on International Joint Conferences on Artificial Intelligence, pages 2206-2212, 2021.","title":"Can cross entropy loss be robust to label noise?","authors":"Lei Feng, Senlin Shu, Zhuoyi Lin, Fengmao Lv, Li Li, Bo An","year":"2021","venue":"Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2020/305"},{"position":9,"raw":"[Ghiassi et al., 2023a] Amirmasoud Ghiassi, Robert Birke, and Lydia Y Chen. Multi label loss correction against missing and corrupted labels. In Asian Conference on Ma- chine Learning, pages 359-374. PMLR, 2023.","title":"Multi label loss correction against missing and corrupted labels","authors":"Ghiassi","year":"2023","venue":"Asian Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Ghiassi et al., 2023b] Amirmasoud Ghiassi, Cosmin Octa- vian Pene, Robert Birke, and Lydia Y Chen. Trusted loss correction for noisy multi-label learning. In Asian Confer- ence on Machine Learning, pages 343-358. PMLR, 2023.","title":"Trusted loss correction for noisy multi-label learning","authors":"Ghiassi","year":"2023","venue":"Asian Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Ghosh et al., 2017] Aritra Ghosh, Himanshu Kumar, and P Shanti Sastry. Robust loss functions under label noise for deep neural networks. In Proceedings of the AAAI con- ference on artificial intelligence, volume 31, 2017.","title":"Robust loss functions under label noise for deep neural networks","authors":"Aritra Ghosh, Himanshu Kumar, Shanti Sastry","year":"2017","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In Proceedings of CVPR, pages 770-778, 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"Proceedings of CVPR","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Higashimoto et al., 2024] Ryota Higashimoto, Soh Yoshida, Takashi Horihata, and Mitsuji Muneyasu. Unbiased pseudo-labeling for learning with noisy la- bels. IEICE Transactions on Information and Systems, 107(1):44-48, 2024.","title":"Unbiased pseudo-labeling for learning with noisy labels","authors":"Ryota Higashimoto, Soh Yoshida, Takashi Horihata, Mitsuji Muneyasu","year":"2024","venue":"IEICE Transactions on Information and Systems","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Hu et al., 2021] Zijian Hu, Zhengyu Yang, Xuefeng Hu, and Ram Nevatia. Simple: Similar pseudo label exploita- tion for semi-supervised classification. In CVPR, pages 15099-15108, 2021.","title":"Simple: Similar pseudo label exploitation for semi-supervised classification","authors":"Zijian Hu, Zhengyu Yang, Xuefeng Hu, Ram Nevatia","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Huynh and Elhamifar, 2020] Dat Huynh and Ehsan Elhami- far. Interactive multi-label cnn learning with partial labels. In CVPR, pages 9423-9432, 2020.","title":"Dat Huynh and Ehsan Elhamifar. Interactive multi-label cnn learning with partial labels","authors":"Elhamifar Huynh","year":"2020","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Jiang et al., 2023] Yangbangyan Jiang, Qianqian Xu, Yun- rui Zhao, Zhiyong Yang, Peisong Wen, Xiaochun Cao, and Qingming Huang. Positive-unlabeled learning with la- bel distribution alignment. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.","title":"Positive-unlabeled learning with label distribution alignment","authors":"Yangbangyan Jiang, Qianqian Xu, Yunrui Zhao, Zhiyong Yang, Peisong Wen, Xiaochun Cao, Qingming Huang","year":"2023","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Kim et al., 2022] Youngwook Kim, Jae Myung Kim, Zeynep Akata, and Jungwoo Lee. Large loss matters in weakly supervised multi-label classification. In Proceed- ings of CVPR, pages 14156-14165, 2022.","title":"Large loss matters in weakly supervised multi-label classification","authors":"Kim","year":"2022","venue":"Proceedings of CVPR","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Kiryo et al., 2017] Ryuichi Kiryo, Gang Niu, Marthinus C Du Plessis, and Masashi Sugiyama. Positive-unlabeled learning with non-negative risk estimator. Advances in Neural Information Processing Systems, 30, 2017.","title":"Positive-unlabeled learning with non-negative risk estimator","authors":"Ryuichi Kiryo, Gang Niu, Marthinus C Du Plessis, Masashi Sugiyama","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Lin et al., 2014] Tsung-Yi Lin, Michael Maire, Serge Be- longie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Com- mon objects in context. In The 13th European Conference on Computer Vision, pages 740-755. Springer, 2014.","title":"Microsoft coco: Common objects in context","authors":"Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, C Lawrence, Zitnick","year":"2014","venue":"The 13th European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Lin et al., 2017] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. Focal loss for dense ob- ject detection. In Proceedings of the IEEE international Conference on Computer Vision, pages 2980-2988, 2017.","title":"Focal loss for dense object detection","authors":"Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollár","year":"2017","venue":"Proceedings of the IEEE international Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Liu et al., 2021] Weiwei Liu, Haobo Wang, Xiaobo Shen, and Ivor W Tsang. The emerging trends of multi-label learning. IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 44(11):7955-7974, 2021.","title":"The emerging trends of multi-label learning","authors":"Weiwei Liu, Haobo Wang, Xiaobo Shen, Ivor W Tsang","year":"2021","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Liu et al., 2022] Fengbei Liu, Yu Tian, Yuanhong Chen, Yuyuan Liu, Vasileios Belagiannis, and Gustavo Carneiro. ACPL: Anti-curriculum pseudo-labelling for semi- supervised medical image classification. In CVPR, pages 20697-20706, 2022.","title":"ACPL: Anti-curriculum pseudo-labelling for semisupervised medical image classification","authors":"Fengbei Liu, Yu Tian, Yuanhong Chen, Yuyuan Liu, Vasileios Belagiannis, Gustavo Carneiro","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Liu et al., 2023a] Biao Liu, Jie Wang, Ning Xu, and Xin Geng. Can class-priors help single-positive multi-label learning? arXiv preprint arXiv:2309.13886, 2023.","title":"Can class-priors help single-positive multi-label learning","authors":"Liu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Liu et al., 2023b] Biao Liu, Ning Xu, Jiaqi Lv, and Xin Geng. Revisiting pseudo-label for single-positive multi- label learning. In International Conference on Machine Learning, pages 22249-22265. PMLR, 2023.","title":"Revisiting pseudo-label for single-positive multilabel learning","authors":"Liu","year":"2023","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Lv et al., 2019] Jiaqi Lv, Ning Xu, RenYi Zheng, and Xin Geng. Weakly supervised multi-label learning via label enhancement. In IJCAI, pages 3101-3107, 2019.","title":"Weakly supervised multi-label learning via label enhancement","authors":"Jiaqi Lv, Ning Xu, Renyi Zheng, Xin Geng","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/430"},{"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. PMLR, 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":"[Rastogi and Mortaza, 2021] Reshma Rastogi and Sayed Mortaza. Multi-label classification with missing labels using label correlation and robust structural learning. Knowledge-based Systems, 229:107336, 2021.","title":"Multi-label classification with missing labels using label correlation and robust structural learning","authors":"Mortaza Rastogi, Reshma Rastogi, Sayed Mortaza","year":"2021","venue":"Knowledge-based Systems","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Ridnik et al., 2021] Tal Ridnik, Emanuel Ben-Baruch, Na- dav Zamir, Asaf Noy, Itamar Friedman, Matan Protter, and Lihi Zelnik-Manor. Asymmetric loss for multi-label clas- sification. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 82-91, 2021.","title":"Asymmetric loss for multi-label classification","authors":"Tal Ridnik, Emanuel Ben-Baruch, Nadav Zamir, Asaf Noy, Itamar Friedman, Matan Protter, Lihi Zelnik-Manor","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Russakovsky et al., 2015] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. ImageNet large scale visual recogni- tion challenge. International Journal of Computer Vision, 115:211-252, 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":"International Journal of Computer Vision","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Sun et al., 2022] Ximeng Sun, Ping Hu, and Kate Saenko. Dualcoop: Fast adaptation to multi-label recognition with limited annotations. NeurIPS, 35:30569-30582, 2022.","title":"Dualcoop: Fast adaptation to multi-label recognition with limited annotations","authors":"Sun","year":"2022","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Tarekegn et al., 2021] Adane Nega Tarekegn, Mario Gia- cobini, and Krzysztof Michalak. A review of methods for imbalanced multi-label classification. Pattern Recog- nition, 118:107965, 2021.","title":"A review of methods for imbalanced multi-label classification","authors":"Adane Nega Tarekegn, Mario Giacobini, Krzysztof Michalak","year":"2021","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Verelst et al., 2023] Thomas Verelst, Paul K Rubenstein, Marcin Eichner, Tinne Tuytelaars, and Maxim Berman. Spatial consistency loss for training multi-label classi- fiers from single-label annotations. In Proceedings of the IEEE/CVF Winter Conference on Applications of Com- puter Vision, pages 3879-3889, 2023.","title":"Spatial consistency loss for training multi-label classifiers from single-label annotations","authors":"Thomas Verelst, Marcin Paul K Rubenstein, Tinne Eichner, Maxim Tuytelaars, Berman","year":"2023","venue":"Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Wah et al., 2011] Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. The Caltech- UCSD birds-200-2011 dataset. 2011.","title":"The Caltech-UCSD birds-200-2011 dataset","authors":"Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, Serge Belongie","year":"2011","venue":null,"doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Wang et al., 2019] Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, and James Bailey. Symmetric cross entropy for robust learning with noisy labels. In Proceed- ings of ICCV, pages 322-330, 2019.","title":"Symmetric cross entropy for robust learning with noisy labels","authors":"Wang","year":"2019","venue":"Proceedings of ICCV","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Wang et al., 2022] Haobo Wang, Ruixuan Xiao, Yixuan Li, Lei Feng, Gang Niu, Gang Chen, and Junbo Zhao. Pico: Contrastive label disambiguation for partial label learning. In ICLR, 2022.","title":"Pico: Contrastive label disambiguation for partial label learning","authors":"Wang","year":"2022","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Wang et al., 2023] Ao Wang, Hui Chen, Zijia Lin, Zixuan Ding, Pengzhang Liu, Yongjun Bao, Weipeng Yan, and Guiguang Ding. Hierarchical prompt learning using clip for multi-label classification with single positive labels. In Proceedings of the 31st ACM International Conference on Multimedia, pages 5594-5604, 2023.","title":"Hierarchical prompt learning using clip for multi-label classification with single positive labels","authors":"Ao Wang, Hui Chen, Zijia Lin, Zixuan Ding, Pengzhang Liu, Yongjun Bao, Weipeng Yan, Guiguang Ding","year":"2023","venue":"Proceedings of the 31st ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Wu et al., 2014] Baoyuan Wu, Zhilei Liu, Shangfei Wang, Bao-Gang Hu, and Qiang Ji. Multi-label learning with missing labels. In the 22nd International Conference on Pattern Recognition, pages 1964-1968. IEEE, 2014.","title":"Multi-label learning with missing labels","authors":"Wu","year":"2014","venue":"the 22nd International Conference on Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Xia et al., 2023a] Shiyu Xia, Jiaqi Lv, Ning Xu, Gang Niu, and Xin Geng. Towards effective visual representations for partial-label learning. In Proceedings of CVPR, pages 15589-15598, 2023.","title":"Towards effective visual representations for partial-label learning","authors":"Xia","year":"2023","venue":"Proceedings of CVPR","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Xia et al., 2023b] Xiaobo Xia, Jiankang Deng, Wei Bao, Yuxuan Du, Bo Han, Shiguang Shan, and Tongliang Liu. Holistic label correction for noisy multi-label classifica- tion. In ICCV, pages 1483-1493, 2023.","title":"Holistic label correction for noisy multi-label classification","authors":"Xia","year":"2023","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Xie and Huang, 2018] Ming-Kun Xie and Sheng-Jun Huang. Partial multi-label learning. In Proceedings of AAAI, volume 32, 2018.","title":"Partial multi-label learning","authors":"Huang Xie, Ming-Kun Xie, Sheng-Jun Huang","year":"2018","venue":"Proceedings of AAAI","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Xie and Huang, 2021] Ming-Kun Xie and Sheng-Jun Huang. Partial multi-label learning with noisy label identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(7):3676-3687, 2021.","title":"Partial multi-label learning with noisy label identification","authors":"Huang Xie, Ming-Kun Xie, Sheng-Jun Huang","year":"2021","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Xie et al., 2022] Ming-Kun Xie, Jiahao Xiao, and Sheng- Jun Huang. Label-aware global consistency for multi-label learning with single positive labels. Advances in Neural Information Processing Systems, 35:18430-18441, 2022.","title":"Label-aware global consistency for multi-label learning with single positive labels","authors":"Ming-Kun Xie, Jiahao Xiao, Sheng-Jun Huang","year":"2022","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Xu et al., 2022] Ning Xu, Congyu Qiao, Jiaqi Lv, Xin Geng, and Min-Ling Zhang. One positive label is suffi- cient: Single-positive multi-label learning with label en- hancement. NeurIPS, 35:21765-21776, 2022.","title":"One positive label is sufficient: Single-positive multi-label learning with label enhancement","authors":"Ning Xu, Congyu Qiao, Jiaqi Lv, Xin Geng, Min-Ling Zhang","year":"2022","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zhang and Sabuncu, 2018] Zhilu Zhang and Mert Sabuncu. Generalized cross entropy loss for training deep neural networks with noisy labels. NeurIPS, 31, 2018.","title":"Generalized cross entropy loss for training deep neural networks with noisy labels","authors":"Sabuncu Zhang, Zhilu Zhang, Mert Sabuncu","year":"2018","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Zhang et al., 2021] Youcai Zhang, Yuhao Cheng, Xinyu Huang, Fei Wen, Rui Feng, Yaqian Li, and Yandong Guo. Simple and robust loss design for multi-label learning with missing labels. arXiv preprint arXiv:2112.07368, 2021.","title":"Simple and robust loss design for multi-label learning with missing labels","authors":"Youcai Zhang, Yuhao Cheng, Xinyu Huang, Fei Wen, Rui Feng, Yaqian Li, Yandong Guo","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Zhang et al., 2023] Wenqiao Zhang, Changshuo Liu, Lingze Zeng, Bengchin Ooi, Siliang Tang, and Yueting Zhuang. Learning in imperfect environment: Multi-label classification with long-tailed distribution and partial labels. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1423-1432, 2023.","title":"Learning in imperfect environment: Multi-label classification with long-tailed distribution and partial labels","authors":"Zhang","year":"2023","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Zhao et al., 2022] Xingyu Zhao, Yuexuan An, Ning Xu, and Xin Geng. Fusion label enhancement for multi-label learn- ing. In IJCAI, 2022.","title":"Fusion label enhancement for multi-label learning","authors":"Xingyu Zhao, Yuexuan An, Ning Xu, Xin Geng","year":"2022","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2022/524"},{"position":48,"raw":"[Zhou et al., 2022a] Donghao Zhou, Pengfei Chen, Qiong Wang, Guangyong Chen, and Pheng-Ann Heng. Acknowl- edging the unknown for multi-label learning with single positive labels. In ECCV, pages 423-440. Springer, 2022.","title":"Acknowledging the unknown for multi-label learning with single positive labels","authors":"Zhou","year":"2022","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Zhou et al., 2022b] Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, and Masashi Sugiyama. Adversarial training with complementary la- bels: On the benefit of gradually informative attacks. NeurIPS, 35:23621-23633, 2022.","title":"Adversarial training with complementary labels: On the benefit of gradually informative attacks","authors":"Zhou","year":"2022","venue":"NeurIPS","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}