{"type":"ScholarlyArticle","title":"Learning Assistance from an Adversarial Critic for Multi-Outputs Prediction","authors":[{"name":"Yue Deng","slug":"yue-deng","given":"Yue","family":"Deng","affiliation":"AI Research Center , Samsung Research America"},{"name":"Yilin Shen","slug":"yilin-shen","given":"Yilin","family":"Shen","affiliation":"AI Research Center , Samsung Research America"},{"name":"Hongxia Jin","slug":"hongxia-jin","given":"Hongxia","family":"Jin","affiliation":"AI Research Center , Samsung Research America"}],"abstract":"We introduce an adversarial-critic-and-assistant (ACA) learning framework to improve the performance of existing supervised learning with multiple outputs.  The core contribution of our ACA is  the innovation of two novel modules, i.e. an  `adversarial critic'  and a `collaborative assistant', that are jointly designed to provide  augmenting information for facilitating general learning tasks. Our approach is not intended to be regarded as an emerging competitor for tons of  well-established algorithms in the field. In fact, most existing approaches, while implemented with different learning objectives, can all be adopted as building blocks seamlessly integrated in the ACA framework to accomplish various real-world tasks. We show the performance and generalization ability of ACA on diverse learning tasks including multi-label classification, attributes prediction and sequence-to-sequence generation.","doi":"10.24963/ijcai.2019/688","url":"https://ijcai.org/proceedings/2019/688","pdf":"https://ijcai.org/proceedings/2019/0688.pdf","markdown":"https://ijcai.org/proceedings/2019/688.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/688","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Natural Language Processing","track":"Main track","number":688,"pages":{"first":4954,"last":4960},"keywords":["Machine Learning: Deep Learning","Natural Language Processing: NLP Applications and Tools"],"videos":[],"references":[{"position":1,"raw":"[Bahdanau et al., 2017] Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron Courville, and Yoshua Bengio. An actor-critic algorithm for sequence prediction. ICLR, 2017.","title":"An actor-critic algorithm for sequence prediction","authors":"Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron Courville, Yoshua Bengio","year":"2017","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bhatia et al., 2015] Kush Bhatia, Himanshu Jain, Pu- rushottam Kar, Manik Varma, and Prateek Jain. Sparse local embeddings for extreme multi-label clas- sification. In NIPS, pages 730-738, 2015. [","title":"Sparse local embeddings for extreme multi-label classification","authors":"Bhatia","year":"2015","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"Chua et al., July 8 10 2009]","title":null,"authors":"Chua","year":"0810","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, and Yan-Tao. Zheng. Nus-wide: A real-world web im- age database from national university of singapore. In CIVR, Santorini, Greece., July 8-10, 2009.","title":"Nus-wide: A real-world web image database from national university of singapore","authors":"Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, Yan-Tao Zheng","year":"2009","venue":"CIVR","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Deng et al., 2019] Y. Deng, K. Chen, Y. Shen, and H. Jin. Adversarial multi-label prediction for spoken and visual signal tagging. In ICASSP 2019 -2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3252-3256, May 2019.","title":"Adversarial multi-label prediction for spoken and visual signal tagging","authors":"Y Deng, K Chen, Y Shen, H Jin","year":"2019","venue":"ICASSP 2019 -2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Dong and Lapata, 2016] Li Dong and Mirella Lapata. Language to logical form with neural attention. ACL, 2016.","title":"Language to logical form with neural attention","authors":"Li Dong, Mirella Lapata","year":"2016","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Gong et al., 2013] Yunchao Gong, Yangqing Jia, Thomas Leung, Alexander Toshev, and Sergey Ioffe. Deep convolutional ranking for multilabel image annotation. arXiv:1312.4894, 2013.","title":"Deep convolutional ranking for multilabel image annotation","authors":"Gong","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Goodfellow et al., 2014] Ian Goodfellow, Jean Pouget- Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In NIPS, pages 2672- 2680, 2014.","title":"Generative adversarial nets","authors":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio","year":"2014","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Guillaumin et al., 2009] M. Guillaumin, T. Mensink, J. Verbeek, and C. Schmid. Tagprop: Discriminative metric learning in nearest neighbor models for image auto-annotation. In ICCV, pages 309-316, Sept 2009.","title":"Tagprop: Discriminative metric learning in nearest neighbor models for image auto-annotation","authors":"M Guillaumin, T Mensink, J Verbeek, C Schmid","year":"2009","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Gygli et al., 2017] Michael Gygli, Mohammad Norouzi, and Anelia Angelova. Deep value networks learn to evaluate and iteratively refine structured outputs. ICML, 2017.","title":"Deep value networks learn to evaluate and iteratively refine structured outputs","authors":"Michael Gygli, Mohammad Norouzi, Anelia Angelova","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Hemphill et al., 1990] Charles T Hemphill, John J Godfrey, and George R Doddington. The atis spoken language systems pilot corpus. In Speech and Natural Language: Proceedings of a Workshop Held at Hidden Valley, Pennsylvania, June 24-27, 1990, 1990.","title":"The atis spoken language systems pilot corpus","authors":"John J Charles T Hemphill, George R Godfrey, Doddington","year":"1990","venue":"Speech and Natural Language: Proceedings of a Workshop Held at Hidden Valley","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Kalchbrenner and Blunsom, 2013] Nal Kalchbrenner and Phil Blunsom. Recurrent continuous translation models. In EMNLP, volume 3, page 413, 2013.","title":"Recurrent continuous translation models","authors":"Nal Kalchbrenner, Phil Blunsom","year":"2013","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kingma and Ba, 2014] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv:1412.6980, 2014.","title":"Adam: A method for stochastic optimization","authors":"Diederik Kingma, Jimmy Ba","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Lin et al., 2014] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 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":"ECCV","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Loza Mencía and Fürnkranz, 2008] Eneldo Loza Mencía and Johannes Fürnkranz. Efficient pairwise multilabel classification for large-scale problems in the legal domain. Machine Learning and Knowledge Discovery in Databases, pages 50-65, 2008.","title":"Efficient pairwise multilabel classification for large-scale problems in the legal domain","authors":"Loza Mencía, Fürnkranz ; Eneldo Loza, Mencía, Johannes Fürnkranz","year":"2008","venue":"Machine Learning and Knowledge Discovery in Databases","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Luong et al., 2015] Minh-Thang Luong, Hieu Pham, and Christopher D Manning. Effective approaches to attention-based neural machine translation. EMNLP, 2015.","title":"Effective approaches to attention-based neural machine translation","authors":"Minh-Thang Luong, Hieu Pham, Christopher D Manning","year":"2015","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Rennie et al., 2016] Steven J Rennie, Etienne Marcheret, Youssef Mroueh, Jarret Ross, and Vaibhava Goel. Self-critical sequence training for image captioning. arXiv:1612.00563, 2016.","title":"Self-critical sequence training for image captioning","authors":"Etienne Steven J Rennie, Youssef Marcheret, Jarret Mroueh, Vaibhava Ross, Goel","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Tai and Lin, 2012] Farbound Tai and Hsuan-Tien Lin. Multilabel classification with principal label space transformation. Neural Computation, 24(9):2508- 2542, 2012.","title":"Multilabel classification with principal label space transformation","authors":"Lin ; Tai, Farbound Tai, Hsuan-Tien Lin","year":"2012","venue":"Neural Computation","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Tsoumakas et al., 2008] Grigorios Tsoumakas, Ioannis Katakis, and Ioannis Vlahavas. Effective and efficient multilabel classification in domains with large number of labels. In ECML/PKDD, pages 30-44, 2008.","title":"Effective and efficient multilabel classification in domains with large number of labels","authors":"Grigorios Tsoumakas, Ioannis Katakis, Ioannis Vlahavas","year":"2008","venue":"ECML/PKDD","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Vinyals et al., 2015] Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan. Show and tell: A neural image caption generator. In CVPR, pages 3156-3164, 2015.","title":"Show and tell: A neural image caption generator","authors":"Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan","year":"2015","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":21,"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":22,"raw":"[Wang et al., 2016] Jiang Wang, Yi Yang, Junhua Mao, Zhiheng Huang, Chang Huang, and Wei Xu. Cnn-rnn: A unified framework for multi-label image classifica- tion. In CVPR, pages 2285-2294, 2016.","title":"Cnn-rnn: A unified framework for multi-label image classification","authors":"Jiang Wang, Yi Yang, Junhua Mao, Zhiheng Huang, Chang Huang, Wei Xu","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Yeh et al., 2017] Chih-Kuan Yeh, Wei-Chieh Wu, Wei- Jen Ko, and Yu-Chiang Frank Wang. Learning deep latent spaces for multi-label classification. 2017.","title":"Learning deep latent spaces for multi-label classification","authors":"Chih-Kuan Yeh, Wei-Chieh Wu, Wei-Jen Ko, Yu-Chiang Frank, Wang","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Yu et al., 2014] Hsiang-Fu Yu, Prateek Jain, Purushot- tam Kar, and Inderjit Dhillon. Large-scale multi-label learning with missing labels. In ICML, pages 593-601, 2014.","title":"Large-scale multi-label learning with missing labels","authors":"Hsiang-Fu Yu, Prateek Jain, Purushottam Kar, Inderjit Dhillon","year":"2014","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Yu et al., 2017] Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. Seqgan: Sequence generative adversar- ial nets with policy gradient. In AAAI, pages 2852- 2858, 2017.","title":"Seqgan: Sequence generative adversarial nets with policy gradient","authors":"Lantao Yu, Weinan Zhang, Jun Wang, Yong Yu","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zhang and Zhou, 2006] Min-Ling Zhang and Zhi-Hua Zhou. Multilabel neural networks with applications to functional genomics and text categorization. TKDE, 18(10):1338-1351, 2006.","title":"Multilabel neural networks with applications to functional genomics and text categorization","authors":"Zhou ; ] Min-Ling Zhang, Zhi-Hua Zhang, Zhou","year":"2006","venue":"TKDE","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}