{"type":"ScholarlyArticle","title":"Synthesizing Samples for Zero-shot Learning","authors":[{"name":"Yuchen Guo","slug":"yuchen-guo","given":"Yuchen","family":"Guo","affiliation":"† School of Software , Tsinghua University , Beijing 100084 , China"},{"name":"Guiguang Ding","slug":"guiguang-ding","given":"Guiguang","family":"Ding","affiliation":"† School of Software , Tsinghua University , Beijing 100084 , China"},{"name":"Jungong Han","slug":"jungong-han","given":"Jungong","family":"Han","affiliation":"‡ School of Computing & Communications , Lancaster University , UK"},{"name":"Yue Gao","slug":"yue-gao","given":"Yue","family":"Gao","affiliation":"† School of Software , Tsinghua University , Beijing 100084 , China"}],"abstract":"Zero-shot learning (ZSL) is to construct recognition models for unseen target classes that have no labeled samples for training. It utilizes the class attributes or semantic vectors as side information and transfers supervision information from related source classes with abundant labeled samples. Existing ZSL approaches adopt an intermediary embedding space to measure the similarity between a sample and the attributes of a target class to perform zero-shot classification. However, this way may suffer from the information loss caused by the embedding process and the similarity measure cannot fully make use of the data distribution. In this paper, we propose a novel approach which turns the ZSL problem into a conventional supervised learning problem by synthesizing samples for the unseen classes. Firstly, the probability distribution of an unseen class is estimated by using the knowledge from seen classes and the class attributes. Secondly, the samples are synthesized based on the distribution for the unseen class. Finally, we can train any supervised classifiers based on the synthesized samples. Extensive experiments on benchmarks demonstrate the superiority of the proposed approach to the state-of-the-art ZSL approaches.","doi":"10.24963/ijcai.2017/246","url":"https://ijcai.org/proceedings/2017/246","pdf":"https://ijcai.org/proceedings/2017/0246.pdf","markdown":"https://ijcai.org/proceedings/2017/246.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/246","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":246,"pages":{"first":1774,"last":1780},"keywords":["Machine Learning: Classification","Machine Learning: Feature Selection/Construction","Machine Learning: Transfer, Adaptation, Multi-task Learning","Robotics and Vision: Robotics and Vision"],"videos":[],"references":[{"position":1,"raw":"[Akata et al., 2015] Zeynep Akata, Scott E. Reed, Daniel Walter, Honglak Lee, and Bernt Schiele. Evaluation of output embed- dings for fine-grained image classification. In CVPR, 2015.","title":"Evaluation of output embeddings for fine-grained image classification","authors":"Zeynep Akata, Scott E Reed, Daniel Walter, Honglak Lee, Bernt Schiele","year":"2015","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Al-Halah et al., 2016] Ziad Al-Halah, Makarand Tapaswi, and Rainer Stiefelhagen. Recovering the missing link: Predicting class-attribute associations for unsupervised zero-shot learning. In CVPR, 2016.","title":"Recovering the missing link: Predicting class-attribute associations for unsupervised zero-shot learning","authors":"Ziad Al-Halah, Makarand Tapaswi, Rainer Stiefelhagen","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Ben-David et al., 2006] Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira. Analysis of representations for domain adaptation. In NIPS, 2006.","title":"Analysis of representations for domain adaptation","authors":"Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira","year":"2006","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bucher et al., 2016] Maxime Bucher, Stéphane Herbin, and Frédéric Jurie. Improving semantic embedding consistency by metric learning for zero-shot classiffication. In ECCV, 2016.","title":"Improving semantic embedding consistency by metric learning for zero-shot classiffication","authors":"Maxime Bucher, Stéphane Herbin, Frédéric Jurie","year":"2016","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Changpinyo et al., 2016] Soravit Changpinyo, Wei-Lun Chao, Bo- qing Gong, and Fei Sha. Synthesized classifiers for zero-shot learning. In CVPR, 2016.","title":"Synthesized classifiers for zero-shot learning","authors":"Changpinyo","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Donahue et al., 2014] Jeff Donahue, Yangqing Jia, Oriol Vinyal- s, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell. Decaf: A deep convolutional activation feature for generic visual recognition. In ICML, 2014.","title":"Decaf: A deep convolutional activation feature for generic visual recognition","authors":"Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell","year":"2014","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Farhadi et al., 2009] Ali Farhadi, Ian Endres, Derek Hoiem, and David A. Forsyth. Describing objects by their attributes. In CVPR, 2009.","title":"Describing objects by their attributes","authors":"Ali Farhadi, Ian Endres, Derek Hoiem, David A Forsyth","year":"2009","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Frome et al., 2013] Andrea Frome, Gregory S. Corrado, Jonathon Shlens, Samy Bengio, Jeffrey Dean, Marc'Aurelio Ranzato, and Tomas Mikolov. Devise: A deep visual-semantic embedding model. In NIPS, 2013.","title":"Devise: A deep visual-semantic embedding model","authors":"Andrea Frome, Gregory S Corrado, Jonathon Shlens, Samy Bengio, Jeffrey Dean, Marc'aurelio Ranzato, Tomas Mikolov","year":"2013","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Fu et al., 2014] Yanwei Fu, Timothy M. Hospedales, Tao Xiang, Zhen-Yong Fu, and Shaogang Gong. Transductive multi-view embedding for zero-shot recognition and annotation. In ECCV, 2014.","title":"Transductive multi-view embedding for zero-shot recognition and annotation","authors":"Yanwei Fu, Timothy M Hospedales, Tao Xiang, Zhen-Yong Fu, Shaogang Gong","year":"2014","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Fu et al., 2015] Zhenyong Fu, Tao Xiang, Elyor Kodirov, and Shaogang Gong. Zero-shot object recognition by semantic man- ifold distance. In CVPR, 2015.","title":"Zero-shot object recognition by semantic manifold distance","authors":"Fu","year":"2015","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Guo et al., 2016] Yuchen Guo, Guiguang Ding, Xiaoming Jin, and Jianmin Wang. Transductive zero-shot recognition via shared model space learning. In AAAI, 2016.","title":"Transductive zero-shot recognition via shared model space learning","authors":"Yuchen Guo, Guiguang Ding, Xiaoming Jin, Jianmin Wang","year":"2016","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Guo et al., 2017a] Yuchen Guo, Guiguang Ding, Jungong Han, and Yue Gao. Zero-shot learning with transferred samples. IEEE TIP, 26(7):3277-3290, 2017.","title":"Zero-shot learning with transferred samples","authors":"Guo","year":"2017","venue":"IEEE TIP","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Guo et al., 2017b] Yuchen Guo, Guiguang Ding, Jungong Han, and Yue Gao. Zero-shot recognition via direct classifier learn- ing with transferred samples and pseudo labels. In AAAI, 2017.","title":"Zero-shot recognition via direct classifier learning with transferred samples and pseudo labels","authors":"Guo","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[He and Garcia, 2009] Haibo He and Edwardo A. Garcia. Learning from imbalanced data. IEEE TKDE, 2009.","title":"Learning from imbalanced data","authors":"Haibo He, Edwardo A Garcia","year":"2009","venue":"IEEE TKDE","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, 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":16,"raw":"[Jayaraman and Grauman, 2014] Dinesh Jayaraman and Kristen Grauman. Zero-shot recognition with unreliable attributes. In NIPS, pages 3464-3472, 2014.","title":"Zero-shot recognition with unreliable attributes","authors":"Dinesh Jayaraman, Kristen Grauman","year":"2014","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Kodirov et al., 2015] Elyor Kodirov, Tao Xiang, Zhenyong Fu, and Shaogang Gong. Unsupervised domain adaptation for zero-shot learning. In ICCV, 2015.","title":"Unsupervised domain adaptation for zero-shot learning","authors":"Kodirov","year":"2015","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Krizhevsky et al., 2012] Alex Krizhevsky, Ilya Sutskever, and Ge- offrey E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.","title":"Imagenet classification with deep convolutional neural networks","authors":"Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton","year":"2012","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Kwitt et al., 2016] Roland Kwitt, Sebastian Hegenbart, and Marc Niethammer. One-shot learning of scene locations via feature trajectory transfer. In CVPR, 2016.","title":"One-shot learning of scene locations via feature trajectory transfer","authors":"Kwitt","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Lampert et al., 2014] Christoph H. Lampert, Hannes Nickisch, and Stefan Harmeling. Attribute-based classification for zero- shot visual object categorization. IEEE TPAMI, 2014.","title":"Attribute-based classification for zeroshot visual object categorization","authors":"Christoph H Lampert, Hannes Nickisch, Stefan Harmeling","year":"2014","venue":"IEEE TPAMI","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Lazaridou et al., 2015] Angeliki Lazaridou, Georgiana Dinu, and Marco Baroni. Hubness and pollution: Delving into cross-space mapping for zero-shot learning. In ACL, 2015.","title":"Hubness and pollution: Delving into cross-space mapping for zero-shot learning","authors":"Angeliki Lazaridou, Georgiana Dinu, Marco Baroni","year":"2015","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Miller et al., 2000] Erik G. Miller, Nicholas E. Matsakis, and Paul A. Viola. Learning from one example through shared densi- ties on transforms. In CVPR, 2000.","title":"Learning from one example through shared densities on transforms","authors":"Erik G Miller, Nicholas E Matsakis, Paul A Viola","year":"2000","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Pan and Yang, 2010] Sinno Jialin Pan and Qiang Yang. A survey on transfer learning. IEEE TKDE, 2010.","title":"A survey on transfer learning","authors":"Jialin Sinno, Qiang Pan, Yang","year":"2010","venue":"IEEE TKDE","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Patterson and Hays, 2012] Genevieve Patterson and James Hays. SUN attribute database: Discovering, annotating, and recogniz- ing scene attributes. In CVPR, 2012.","title":"SUN attribute database: Discovering, annotating, and recognizing scene attributes","authors":"Genevieve Patterson, James Hays","year":"2012","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Romera-Paredes and Torr, 2015] Bernardino Romera-Paredes and Philip H. S. Torr. An embarrassingly simple approach to zero- shot learning. In ICML, 2015.","title":"An embarrassingly simple approach to zeroshot learning","authors":"Romera-Paredes, Torr ; Bernardino Romera-Paredes, Philip H S Torr","year":"2015","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Rosen-Zvi et al., 2010] Michal Rosen-Zvi, Chaitanya Chemudugunta, Thomas L. Griffiths, Padhraic Smyth, and Mark Steyvers. Learning author-topic models from text corpora. ACM TIST, 2010.","title":"Learning author-topic models from text corpora","authors":"Michal Rosen-Zvi, Chaitanya Chemudugunta, Thomas L Griffiths, Padhraic Smyth, Mark Steyvers","year":"2010","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Simonyan and Zisserman, 2014] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale im- age recognition. CoRR, abs/1409.1556, 2014.","title":"Very deep convolutional networks for large-scale image recognition","authors":"Karen Simonyan, Andrew Zisserman","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Socher et al., 2013] Richard Socher, Milind Ganjoo, Christo- pher D. Manning, and Andrew Y. Ng. Zero-shot learning through cross-modal transfer. In NIPS, 2013.","title":"Zero-shot learning through cross-modal transfer","authors":"Richard Socher, Milind Ganjoo, Christopher D Manning, Andrew Y Ng","year":"2013","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Van der Maaten and Hinton, 2008] Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. JMLR, 2008.","title":"Visualizing data using t-sne","authors":"Van Der Maaten, Laurens Hinton, Geoffrey Van Der Maaten, Hinton","year":"2008","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Wah et al., 2011] Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. The Caltech-UCSD Birds- 200-2011 Dataset. Technical report, 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":31,"raw":"[Wang et al., 2010] Jinjun Wang, Jianchao Yang, Kai Yu, Fengjun Lv, Thomas S. Huang, and Yihong Gong. Locality-constrained linear coding for image classification. In CVPR, 2010.","title":"Locality-constrained linear coding for image classification","authors":"Jinjun Wang, Jianchao Yang, Kai Yu, Fengjun Lv, Thomas S Huang, Yihong Gong","year":"2010","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Xian et al., 2016] Yongqin Xian, Zeynep Akata, Gaurav Sharma, Quynh N. Nguyen, Matthias Hein, and Bernt Schiele. Latent embeddings for zero-shot classification. In CVPR, 2016.","title":"Latent embeddings for zero-shot classification","authors":"Xian","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Yu and Aloimonos, 2010] Xiaodong Yu and Yiannis Aloimonos. Attribute-based transfer learning for object categorization with zero/one training example. In ECCV, 2010.","title":"Attribute-based transfer learning for object categorization with zero/one training example","authors":"Xiaodong Yu, Yiannis Aloimonos","year":"2010","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Zhang and Saligrama, 2015] Ziming Zhang and Venkatesh Saligrama. Zero-shot learning via semantic similarity embed- ding. In ICCV, 2015.","title":"Zero-shot learning via semantic similarity embedding","authors":"Ziming Zhang, Venkatesh Saligrama","year":"2015","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Zhang and Saligrama, 2016a] Ziming Zhang and Venkatesh Saligrama. Zero-shot learning via joint latent similarity embedding. In CVPR, 2016.","title":"Zero-shot learning via joint latent similarity embedding","authors":"Saligrama Zhang, Ziming Zhang, Venkatesh Saligrama","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Zhang and Saligrama, 2016b] Ziming Zhang and Venkatesh Saligrama. Zero-shot recognition via structured prediction. In ECCV, 2016.","title":"Zero-shot recognition via structured prediction","authors":"Saligrama Zhang, Ziming Zhang, Venkatesh Saligrama","year":"2016","venue":"ECCV","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}