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Pattern recognition, Vol. 37, 9 (2004), 1757--1771."},{"key":"e_1_3_2_2_3_1","volume-title":"Generating sentences from a continuous space. arXiv preprint arXiv:1511.06349","author":"Bowman Samuel R","year":"2015","unstructured":"Samuel R Bowman , Luke Vilnis , Oriol Vinyals , Andrew M Dai , Rafal Jozefowicz , and Samy Bengio . 2015. Generating sentences from a continuous space. arXiv preprint arXiv:1511.06349 ( 2015 ). Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio. 2015. Generating sentences from a continuous space. arXiv preprint arXiv:1511.06349 (2015)."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"crossref","unstructured":"Zhao-Min Chen Xiu-Shen Wei Peng Wang and Yanwen Guo. 2019. Multi-Label Image Recognition with Graph Convolutional Networks. In CVPR. 5177--5186.  Zhao-Min Chen Xiu-Shen Wei Peng Wang and Yanwen Guo. 2019. Multi-Label Image Recognition with Graph Convolutional Networks. 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In NeurIPS. 2121--2129.","author":"Frome Andrea","year":"2013","unstructured":"Andrea Frome , Greg S Corrado , Jon Shlens , Samy Bengio , Jeff Dean , Marc'Aurelio Ranzato , and Tomas Mikolov . 2013 . Devise: A deep visual-semantic embedding model. In NeurIPS. 2121--2129. Andrea Frome, Greg S Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc'Aurelio Ranzato, and Tomas Mikolov. 2013. Devise: A deep visual-semantic embedding model. In NeurIPS. 2121--2129."},{"key":"e_1_3_2_2_7_1","unstructured":"Jonas Gehring Michael Auli David Grangier Denis Yarats and Yann N Dauphin. 2017. Convolutional sequence to sequence learning. In ICML. 1243--1252.  Jonas Gehring Michael Auli David Grangier Denis Yarats and Yann N Dauphin. 2017. Convolutional sequence to sequence learning. In ICML. 1243--1252."},{"key":"e_1_3_2_2_8_1","unstructured":"Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative adversarial nets. In NeurIPS. 2672--2680.  Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative adversarial nets. In NeurIPS. 2672--2680."},{"key":"e_1_3_2_2_9_1","unstructured":"Yves Grandvalet and Yoshua Bengio. 2005. Semi-supervised learning by entropy minimization. In NeurIPS. 529--536.  Yves Grandvalet and Yoshua Bengio. 2005. Semi-supervised learning by entropy minimization. In NeurIPS. 529--536."},{"key":"e_1_3_2_2_10_1","unstructured":"Jiangfan Han Ping Luo and Xiaogang Wang. 2019. Deep self-learning from noisy labels. In CVPR. 5138--5147.  Jiangfan Han Ping Luo and Xiaogang Wang. 2019. Deep self-learning from noisy labels. In CVPR. 5138--5147."},{"key":"e_1_3_2_2_11_1","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. 770--778.  Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. 770--778."},{"key":"e_1_3_2_2_12_1","volume-title":"Long short-term memory. Neural computation","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber . 1997. Long short-term memory. Neural computation , Vol. 9 , 8 ( 1997 ), 1735--1780. Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation, Vol. 9, 8 (1997), 1735--1780."},{"key":"e_1_3_2_2_13_1","unstructured":"Zhiting Hu Zichao Yang Xiaodan Liang Ruslan Salakhutdinov and Eric P Xing. 2017. Toward controlled generation of text. In ICML. JMLR. org 1587--1596.  Zhiting Hu Zichao Yang Xiaodan Liang Ruslan Salakhutdinov and Eric P Xing. 2017. Toward controlled generation of text. In ICML. JMLR. org 1587--1596."},{"key":"e_1_3_2_2_14_1","volume-title":"Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition. In Workshop on Deep Learning, NIPS.","author":"Jaderberg Max","year":"2014","unstructured":"Max Jaderberg , Karen Simonyan , Andrea Vedaldi , and Andrew Zisserman . 2014 . Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition. In Workshop on Deep Learning, NIPS. Max Jaderberg, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014. Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition. In Workshop on Deep Learning, NIPS."},{"key":"e_1_3_2_2_15_1","volume-title":"Xing","author":"Kampffmeyer Michael","year":"2019","unstructured":"Michael Kampffmeyer , Yinbo Chen , Xiaodan Liang , Hao Wang , Yujia Zhang , and Eric P . Xing . 2019 . Rethinking Knowledge Graph Propagation for Zero-Shot Learning. In CVPR. Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang, Hao Wang, Yujia Zhang, and Eric P. Xing. 2019. Rethinking Knowledge Graph Propagation for Zero-Shot Learning. 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Auto-Encoding Variational Bayes. arxiv: stat.ML\/1312.6114  Diederik P Kingma and Max Welling. 2013. Auto-Encoding Variational Bayes. arxiv: stat.ML\/1312.6114"},{"key":"e_1_3_2_2_19_1","unstructured":"Alex Krizhevsky Ilya Sutskever and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In NeurIPS. 1097--1105.  Alex Krizhevsky Ilya Sutskever and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In NeurIPS. 1097--1105."},{"key":"e_1_3_2_2_20_1","unstructured":"Vinay Kumar Verma Gundeep Arora Ashish Mishra and Piyush Rai. 2018. Generalized Zero-Shot Learning via Synthesized Examples. In CVPR.  Vinay Kumar Verma Gundeep Arora Ashish Mishra and Piyush Rai. 2018. Generalized Zero-Shot Learning via Synthesized Examples. In CVPR."},{"key":"e_1_3_2_2_21_1","unstructured":"Chung-Wei Lee Wei Fang Chih-Kuan Yeh and Yu-Chiang Frank Wang. 2018. Multi-label zero-shot learning with structured knowledge graphs. In CVPR. 1576--1585.  Chung-Wei Lee Wei Fang Chih-Kuan Yeh and Yu-Chiang Frank Wang. 2018. Multi-label zero-shot learning with structured knowledge graphs. In CVPR. 1576--1585."},{"key":"e_1_3_2_2_22_1","volume-title":"Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025","author":"Luong Minh-Thang","year":"2015","unstructured":"Minh-Thang Luong , Hieu Pham , and Christopher D Manning . 2015. Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 ( 2015 ). Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015. Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015)."},{"key":"e_1_3_2_2_23_1","volume-title":"Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784","author":"Mirza Mehdi","year":"2014","unstructured":"Mehdi Mirza and Simon Osindero . 2014. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 ( 2014 ). Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)."},{"key":"e_1_3_2_2_24_1","volume-title":"CVPR Workshops.","author":"Mishra Ashish","unstructured":"Ashish Mishra , Shiva Krishna Reddy , Anurag Mittal , and Hema A. Murthy . 2018. A Generative Model for Zero Shot Learning Using Conditional Variational Autoencoders . In CVPR Workshops. Ashish Mishra, Shiva Krishna Reddy, Anurag Mittal, and Hema A. Murthy. 2018. A Generative Model for Zero Shot Learning Using Conditional Variational Autoencoders. In CVPR Workshops."},{"key":"e_1_3_2_2_25_1","unstructured":"Augustus Odena Christopher Olah and Jonathon Shlens. 2017. Conditional image synthesis with auxiliary classifier gans. In ICML. JMLR. org 2642--2651.  Augustus Odena Christopher Olah and Jonathon Shlens. 2017. Conditional image synthesis with auxiliary classifier gans. In ICML. 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Dsgan: Generative adversarial training for distant supervision relation extraction. arXiv preprint arXiv:1805.09929 (2018)."},{"key":"e_1_3_2_2_28_1","first-page":"269","article-title":"Snorkel: Rapid training data creation with weak supervision","volume":"11","author":"Ratner Alexander","year":"2017","unstructured":"Alexander Ratner , Stephen H Bach , Henry Ehrenberg , Jason Fries , Sen Wu , and Christopher R\u00e9 . 2017 . Snorkel: Rapid training data creation with weak supervision . In VLDB , Vol. 11. 269 . Alexander Ratner, Stephen H Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher R\u00e9. 2017. Snorkel: Rapid training data creation with weak supervision. In VLDB, Vol. 11. 269.","journal-title":"VLDB"},{"key":"e_1_3_2_2_29_1","unstructured":"Shaoqing Ren Kaiming He Ross Girshick and Jian Sun. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. In NeurIPS. 91--99.  Shaoqing Ren Kaiming He Ross Girshick and Jian Sun. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. In NeurIPS. 91--99."},{"key":"e_1_3_2_2_30_1","unstructured":"Bernardino Romera-Paredes and Philip Torr. 2015. An embarrassingly simple approach to zero-shot learning. In ICML. 2152--2161.  Bernardino Romera-Paredes and Philip Torr. 2015. An embarrassingly simple approach to zero-shot learning. In ICML. 2152--2161."},{"key":"e_1_3_2_2_31_1","volume-title":"International workshop on simulation and synthesis in medical imaging. 1--11","author":"Shin Hoo-Chang","unstructured":"Hoo-Chang Shin , Neil A Tenenholtz , Jameson K Rogers , Christopher G Schwarz , Matthew L Senjem , Jeffrey L Gunter , Katherine P Andriole , and Mark Michalski . [n.d.]. Medical image synthesis for data augmentation and anonymization using generative adversarial networks . In International workshop on simulation and synthesis in medical imaging. 1--11 . Hoo-Chang Shin, Neil A Tenenholtz, Jameson K Rogers, Christopher G Schwarz, Matthew L Senjem, Jeffrey L Gunter, Katherine P Andriole, and Mark Michalski. [n.d.]. Medical image synthesis for data augmentation and anonymization using generative adversarial networks. In International workshop on simulation and synthesis in medical imaging. 1--11."},{"key":"e_1_3_2_2_32_1","volume-title":"Deep headline generation for clickbait detection","author":"Shu Kai","unstructured":"Kai Shu , Suhang Wang , Thai Le , Dongwon Lee , and Huan Liu . 2018. Deep headline generation for clickbait detection . In ICDM. IEEE , 467--476. Kai Shu, Suhang Wang, Thai Le, Dongwon Lee, and Huan Liu. 2018. Deep headline generation for clickbait detection. In ICDM. IEEE, 467--476."},{"key":"e_1_3_2_2_33_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman . 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 ( 2014 ). Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"key":"e_1_3_2_2_34_1","unstructured":"Kihyuk Sohn Honglak Lee and Xinchen Yan. 2015. Learning structured output representation using deep conditional generative models. In NeurIPS. 3483--3491.  Kihyuk Sohn Honglak Lee and Xinchen Yan. 2015. Learning structured output representation using deep conditional generative models. In NeurIPS. 3483--3491."},{"key":"e_1_3_2_2_35_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In NeurIPS. 5998--6008.  Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. 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Global-and-Local Aware Data Generation for the Class Imbalance Problem. In SDM. SIAM, 307--315."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"crossref","unstructured":"Xiaolong Wang Yufei Ye and Abhinav Gupta. 2018b. Zero-Shot Recognition via Semantic Embeddings and Knowledge Graphs. In CVPR.  Xiaolong Wang Yufei Ye and Abhinav Gupta. 2018b. Zero-Shot Recognition via Semantic Embeddings and Knowledge Graphs. In CVPR.","DOI":"10.1109\/CVPR.2018.00717"},{"key":"e_1_3_2_2_41_1","volume-title":"Weakly supervised facial attribute manipulation via deep adversarial network","author":"Wang Yilin","unstructured":"Yilin Wang , Suhang Wang , Guojun Qi , Jiliang Tang , and Baoxin Li. 2018a. Weakly supervised facial attribute manipulation via deep adversarial network . In WACV. IEEE , 112--121. Yilin Wang, Suhang Wang, Guojun Qi, Jiliang Tang, and Baoxin Li. 2018a. Weakly supervised facial attribute manipulation via deep adversarial network. In WACV. 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Unsupervised data augmentation for consistency training. (2019).  Qizhe Xie Zihang Dai Eduard Hovy Minh-Thang Luong and Quoc V Le. 2019. Unsupervised data augmentation for consistency training. (2019)."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"crossref","unstructured":"Li Zhang Tao Xiang and Shaogang Gong. 2017. Learning a deep embedding model for zero-shot learning. In CVPR. 2021--2030.  Li Zhang Tao Xiang and Shaogang Gong. 2017. Learning a deep embedding model for zero-shot learning. In CVPR. 2021--2030.","DOI":"10.1109\/CVPR.2017.321"},{"key":"e_1_3_2_2_46_1","volume-title":"A brief introduction to weakly supervised learning. National science review","author":"Zhou Zhi-Hua","year":"2018","unstructured":"Zhi-Hua Zhou . 2018. A brief introduction to weakly supervised learning. National science review , Vol. 5 , 1 ( 2018 ), 44--53. Zhi-Hua Zhou. 2018. A brief introduction to weakly supervised learning. 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