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This paper addresses the task of learning an image clas-sifier when some categories are defined by semantic descriptions only (e.g.
Gradient-based learning applied to document recognition
Y LeCun, L Bottou, Y Bengio, and P Haffner · 1998
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Learning from imbalanced data sets with boosting and data generation - the DataBoost-IM approach
Hongyu Guo and Herna L Viktor · 2004
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Generative versus Discriminative Methods for Object Recognition
Ilkay Ulusoy and Christopher M Bishop · 2005
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Describing objects by their attributes
Ali Farhadi, Ian Endres, Derek Hoiem, and David Forsyth · 2009
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Wsabie: scaling up to large vocabulary image annotation
Jason Weston, Samy Bengio, and Nicolas Usunier · 2011
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Sun attribute database: Discovering, annotating, and recognizing scene attributes
Genevieve Patterson and James Hays · 2012
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Generalized denoising auto-encoders as generative models
Yoshua Bengio, Li Yao, Guillaume Alain, and Pascal Vincent · 2013
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DeViSE: A Deep Visual-Semantic Embedding Model
Andrea Frome, Gregory S Corrado, Jonathon Shlens, Samy Bengio, Jeffrey Dean, Marc’Aurelio Ranzato, and Tomas Mikolov · 2013
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Rectifier nonlinearities improve neural network acoustic models
Andrew L Maas, Awni Y Hannun, and Andrew Y Ng · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Zero-shot learning by convex combination of semantic embeddings
Mohammad Norouzi, Tomas Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg S Corrado, and Jeffrey Dean · 2013
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Generative adversarial nets
I Goodfellow, J Pouget-Abadie, and M Mirza · 2014
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Zero-shot recognition with unreliable attributes
Dinesh Jayaraman and Kristen Grauman · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Attribute-Based Classification for Zero-Shot Visual Object Categorization
Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling · 2014
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Conditional Generative Adversarial Nets
Mehdi Mirza and Simon Osindero · 2014
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan and Andrew Zisserman · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Zero-Shot Learning via Semantic Similarity Embedding
Ziming Zhang and Venkatesh Saligrama · 2015
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Multi-cue Zero-Shot Learning with Strong Supervision
Zeynep Akata, Mateusz Malinowski, Mario Fritz, and Bernt Schiele · 2016
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Hierarchical classification for dealing with the Class imbalance problem
Mohamed Bahy Bader-El-Den, Eleman Teitei, and Mo Adda · 2016
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Improving semantic embedding consistency by metric learning for zero-shot classiffication
M Bucher, S Herbin, and F Jurie · 2016
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Hard negative mining for metric learning based zero-shot classification
Maxime Bucher, Stéphane Herbin, and Frédéric Jurie · 2016
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Embodied gesture learning from one-shot
Maria E Cabrera and Juan P Wachs · 2016
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Learning deep features for scene recognition using places database
Bolei Zhou, Agata Lapedriza, Jianxiong Xiao, Antonio Torralba, and Aude Oliva · 2014
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Evaluation of Output Embeddings for Fine-Grained Image Classification
Zeynep Akata, Scott Reed, Daniel Walter, Honglak Lee, and Bernt Schiele · 2015
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On the Benefit of Synthetic Data for Company Logo Detection
Christian Eggert, Anton Winschel, and Rainer Lienhart · 2015
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi et al · 2015
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Generative moment matching networks
Yujia Li, Kevin Swersky, and Richard S Zemel · 2015
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Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey · 2015
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Synthesized classifiers for zero-shot learning
Soravit Changpinyo, Wei-Lun Chao, Boqing Gong, and Fei Sha · 2016
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An empirical study and analysis of generalized zero-shot learning for object recognition in the wild
Wei-Lun Chao, Soravit Changpinyo, Boqing Gong, and Fei Sha · 2016
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Mode Regularized Generative Adversarial Networks
Tong Che, Yanran Li, Athul Paul Jacob, Yoshua Bengio, and Wenjie Li · 2016
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Reading Text in the Wild with Convolutional Neural Networks
Max Jaderberg, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2016
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Conditional image synthesis with auxiliary classifier gans
Augustus Odena, Christopher Olah, and Jonathon Shlens · 2016
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Zero-shot visual recognition via bidirectional latent embedding
Qian Wang and Ke Chen · 2016
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Latent embeddings for zero-shot classification
Yongqin Xian, Zeynep Akata, Gaurav Sharma, Quynh Nguyen, Matthias Hein, and Bernt Schiele · 2016
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Zero-shot learning via joint latent similarity embedding
Ziming Zhang and Venkatesh Saligrama · 2016
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Zero-shot learning-the good, the bad and the ugly
Yongqin Xian, Bernt Schiele, and Zeynep Akata · 2017
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