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As a promising area in artificial intelligence, a new learning paradigm, called Small Sample Learning (SSL), has been attracting prominent research attention in the recent years.
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Small sample learning during multimedia retrieval using biasmap
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Learning and evaluating classifiers under sample selection bias
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Causal models: How people think about the world and its alternatives
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Integrating structured biological data by kernel maximum mean discrepancy
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One-shot learning of object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2006
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Reducing the dimensionality of data with neural networks
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Correcting sample selection bias by unlabeled data
Jiayuan Huang, Arthur Gretton, Karsten M Borgwardt, Bernhard Schölkopf, and Alex J Smola · 2007
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Cross-domain video concept detection using adaptive svms
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Cross-domain learning methods for high-level visual concept classification
Wei Jiang, Eric Zavesky, Shih-Fu Chang, and Alex Loui · 2008
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Direct importance estimation with model selection and its application to covariate shift adaptation
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Curriculum learning
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Speech-driven facial animation using a shared gaussian process latent variable model
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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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Domain transfer svm for video concept detection
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Describing objects by their attributes
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The construction system of the brain
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Learning from imbalanced data
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Efficient direct density ratio estimation for non-stationarity adaptation and outlier detection
Takafumi Kanamori, Shohei Hido, and Masashi Sugiyama · 2009
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Learning to detect unseen object classes by between-class attribute transfer
Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling · 2009
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Zero-shot learning with semantic output codes
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A new learning paradigm: Learning using privileged information
Vladimir Vapnik and Akshay Vashist · 2009
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The one-shot similarity kernel
Lior Wolf, Tal Hassner, and Yaniv Taigman · 2009
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Domain adaptation problems: A dasvm classification technique and a circular validation strategy
Lorenzo Bruzzone and Mattia Marconcini · 2010
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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Semantic label sharing for learning with many categories
Rob Fergus, Hector Bernal, Yair Weiss, and Antonio Torralba · 2010
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Learning fast approximations of sparse coding
Karol Gregor and Yann LeCun · 2010
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Self-paced learning for latent variable models
M Pawan Kumar, Benjamin Packer, and Daphne Koller · 2010
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Play it again: reactivation of waking experience and memory
Joseph O’Neill, Barty Pleydell-Bouverie, David Dupret, and Jozsef Csicsvari · 2010
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2010
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Hubs in space: Popular nearest neighbors in high-dimensional data
Miloš Radovanović, Alexandros Nanopoulos, and Mirjana Ivanović · 2010
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What helps where–and why? semantic relatedness for knowledge transfer
Marcus Rohrbach, Michael Stark, György Szarvas, Iryna Gurevych, and Bernt Schiele · 2010
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Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
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Two-dimensional polar harmonic transforms for invariant image representation
Pew-Thian Yap, Xudong Jiang, and Alex Chichung Kot · 2010
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Attribute-based transfer learning for object categorization with zero/one training example
Xiaodong Yu and Yiannis Aloimonos · 2010
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Strategies for training large scale neural network language models
Tomáš Mikolov, Anoop Deoras, Daniel Povey, Lukáš Burget, and Jan Černockỳ · 2011
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Relative attributes
Devi Parikh and Kristen Grauman · 2011
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Learning to share visual appearance for multiclass object detection
Ruslan Salakhutdinov, Antonio Torralba, and Josh Tenenbaum · 2011
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How to grow a mind: Statistics, structure, and abstraction
Joshua B Tenenbaum, Charles Kemp, Thomas L Griffiths, and Noah D Goodman · 2011
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Visual search in scenes involves selective and nonselective pathways
Jeremy M Wolfe, Melissa L-H Võ, Karla K Evans, and Michelle R Greene · 2011
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Exploiting web images for event recognition in consumer videos: A multiple source domain adaptation approach
Lixin Duan, Dong Xu, and Shih-Fu Chang · 2012
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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The future of memory: remembering, imagining, and the brain
Daniel L Schacter, Donna Rose Addis, Demis Hassabis, Victoria C Martin, R Nathan Spreng, and Karl K Szpunar · 2012
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Information-theoretical learning of discriminative clusters for unsupervised domain adaptation
Yuan Shi and Fei Sha · 2012
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Multimodal learning with deep boltzmann machines
Nitish Srivastava and Ruslan R Salakhutdinov · 2012
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Label-embedding for attribute-based classification
Zeynep Akata, Florent Perronnin, Zaid Harchaoui, and Cordelia Schmid · 2013
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Expressive visual text-to-speech using active appearance models
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Simulation as an engine of physical scene understanding
Peter W Battaglia, Jessica B Hamrick, and Joshua B Tenenbaum · 2013
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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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New avenues in opinion mining and sentiment analysis
Erik Cambria, Björn Schuller, Yunqing Xia, and Catherine Havasi · 2013
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Goals and habits in the brain
Ray J Dolan and Peter Dayan · 2013
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Write a classifier: Zero-shot learning using purely textual descriptions
Mohamed Elhoseiny, Babak Saleh, and Ahmed Elgammal · 2013
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Devise: A deep visual-semantic embedding model
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Information-seeking, curiosity, and attention: computational and neural mechanisms
Jacqueline Gottlieb, Pierre-Yves Oudeyer, Manuel Lopes, and Adrien Baranes · 2013
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Efficient learning of domain-invariant image representations
Judy Hoffman, Erik Rodner, Jeff Donahue, Trevor Darrell, and Kate Saenko · 2013
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Metric learning: A survey
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Zero-shot learning by convex combination of semantic embeddings
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Deep convolutional neural networks for lvcsr
Tara N Sainath, Abdel-rahman Mohamed, Brian Kingsbury, and Bhuvana Ramabhadran · 2013
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Toward open set recognition
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Sources of uncertainty in intuitive physics
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Zero-shot learning through cross-modal transfer
Richard Socher, Milind Ganjoo, Christopher D Manning, and Andrew Ng · 2013
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One-shot learning gesture recognition from rgb-d data using bag of features
Jun Wan, Qiuqi Ruan, Wei Li, and Shuang Deng · 2013
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A unified probabilistic approach modeling relationships between attributes and objects
Xiaoyang Wang and Qiang Ji · 2013
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Modality propagation: coherent synthesis of subject-specific scans with data-driven regularization
Dong Hye Ye, Darko Zikic, Ben Glocker, Antonio Criminisi, and Ender Konukoglu · 2013
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Designing category-level attributes for discriminative visual recognition
Felix X Yu, Liangliang Cao, Rogerio S Feris, John R Smith, and Shih-Fu Chang · 2013
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Large-scale object classification using label relation graphs
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Transductive multi-view embedding for zero-shot recognition and annotation
Yanwei Fu, Timothy M Hospedales, Tao Xiang, Zhenyong Fu, and Shaogang Gong · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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A multi-view embedding space for modeling internet images, tags, and their semantics
Yunchao Gong, Qifa Ke, Michael Isard, and Svetlana Lazebnik · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
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Zero-shot recognition with unreliable attributes
Dinesh Jayaraman and Kristen Grauman · 2014
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Decorrelating semantic visual attributes by resisting the urge to share
Dinesh Jayaraman, Fei Sha, and Kristen Grauman · 2014
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Auto-encoding variational bayes
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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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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Transfer joint matching for unsupervised domain adaptation
Mingsheng Long, Jianmin Wang, Guiguang Ding, Jiaguang Sun, and Philip S Yu · 2014
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Costa: Co-occurrence statistics for zero-shot classification
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Learning and transferring mid-level image representations using convolutional neural networks
Maxime Oquab, Leon Bottou, Ivan Laptev, and Josef Sivic · 2014
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Internally generated sequences in learning and executing goal-directed behavior
Giovanni Pezzulo, Matthijs AA van der Meer, Carien S Lansink, and Cyriel MA Pennartz · 2014
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Mr to ct registration of brains using image synthesis
Snehashis Roy, Aaron Carass, Amod Jog, Jerry L Prince, and Junghoon Lee · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 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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Vqa: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh · 2015
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Predicting deep zero-shot convolutional neural networks using textual descriptions
Lei Jimmy Ba, Kevin Swersky, Sanja Fidler, and Ruslan Salakhutdinov · 2015
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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Sharing representations for long tail computer vision problems
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Webly supervised learning of convolutional networks
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Attention-based models for speech recognition
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Commonsense reasoning and commonsense knowledge in artificial intelligence
Ernest Davis and Gary Marcus · 2015
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Improving zero-shot learning by mitigating the hubness problem
Georgiana Dinu, Angeliki Lazaridou, and Marco Baroni · 2015
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Unsupervised visual representation learning by context prediction
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Unsupervised domain adaptation by backpropagation
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Fast r-cnn
Ross Girshick · 2015
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Draw: A recurrent neural network for image generation
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Rezende, and Daan Wierstra · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Deep metric learning using triplet network
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Machine learning: Trends, perspectives, and prospects
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Deep visual-semantic alignments for generating image descriptions
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Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov · 2015
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Unsupervised domain adaptation for zero-shot learning
Elyor Kodirov, Tao Xiang, Zhenyong Fu, and Shaogang Gong · 2015
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Deep convolutional inverse graphics network
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Metalearning: a survey of trends and technologies
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Learning transferable features with deep adaptation networks
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Human-level control through deep reinforcement learning
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An embarrassingly simple approach to zero-shot learning
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Fitnets: Hints for thin deep nets
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Imagenet large scale visual recognition challenge
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Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver · 2015
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Ridge regression, hubness, and zero-shot learning
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Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
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Unsupervised learning of video representations using lstms
Nitish Srivastava, Elman Mansimov, and Ruslan Salakhudinov · 2015
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End-to-end memory networks
Sainbayar Sukhbaatar, Jason Weston, Rob Fergus, et al · 2015
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Knowledge transfer in deep block-modular neural networks
Alexander V Terekhov, Guglielmo Montone, and J Kevin O¡¯Regan · 2015
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Simultaneous deep transfer across domains and tasks
Eric Tzeng, Judy Hoffman, Trevor Darrell, and Kate Saenko · 2015
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Cross-domain synthesis of medical images using efficient location-sensitive deep network
Hien Van Nguyen, Kevin Zhou, and Raviteja Vemulapalli · 2015
The more you know: Using knowledge graphs for image classification
Kenneth Marino, Ruslan Salakhutdinov, and Abhinav Gupta · 2017
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What objective does self-paced learning indeed optimize?
Deyu Meng, Qian Zhao, and Lu Jiang · 2017
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Learned d-amp: Principled neural network based compressive image recovery
Chris Metzler, Ali Mousavi, and Richard Baraniuk · 2017
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A generative model for zero shot learning using conditional variational autoencoders
Ashish Mishra, M Reddy, Anurag Mittal, and Hema A Murthy · 2017
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Ishan Misra, Abhinav Gupta, and Martial Hebert · 2017
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Lung cancer screening using adaptive memory-augmented recurrent networks
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Unsupervised cross-modal synthesis of subject-specific scans
Raviteja Vemulapalli, Hien Van Nguyen, and Shaohua Kevin Zhou · 2015
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Show and tell: A neural image caption generator
Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan · 2015
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Convolutional lstm network: A machine learning approach for precipitation nowcasting
SHI Xingjian, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo · 2015
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Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio · 2015
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Zero-shot learning via semantic similarity embedding
Ziming Zhang and Venkatesh Saligrama · 2015
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Label-embedding for image classification
Zeynep Akata, Florent Perronnin, Zaid Harchaoui, and Cordelia Schmid · 2016
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Aryan Mobiny, Supratik Moulik, Ilker Gurcan, Tanay Shah, and Hien Van Nguyen · 2017
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Deepstack: Expert-level artificial intelligence in heads-up no-limit poker
Matej Moravčík, Martin Schmid, Neil Burch, Viliam Lisỳ, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, and Michael Bowling · 2017
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Semantically consistent regularization for zero-shot recognition
Pedro Morgado and Nuno Vasconcelos · 2017
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Tanmoy Mukherjee, Makoto Yamada, and Timothy M Hospedales · 2017
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Meta networks
Tsendsuren Munkhdalai and Hong Yu · 2017
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Meta-learning via feature-label memory network
Dawit Mureja, Hyunsin Park, and Chang D Yoo · 2017
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Zero-shot learning via category-specific visual-semantic mapping
Li Niu, Jianfei Cai, and Ashok Veeraraghavan · 2017
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Conditional image synthesis with auxiliary classifier gans
Augustus Odena, Christopher Olah, and Jonathon Shlens · 2017
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Operational data augmentation in classifying single aerial images of animals
Emmanuel Okafor, Rik Smit, Lambert Schomaker, and Marco Wiering · 2017
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Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell · 2017
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Joint intermodal and intramodal label transfers for extremely rare or unseen classes
Guo-Jun Qi, Wei Liu, Charu Aggarwal, and Thomas Huang · 2017
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Convolutional recurrent neural networks for dynamic mr image reconstruction
Chen Qin, Jo Schlemper, Jose Caballero, Anthony Price, Joseph V Hajnal, and Daniel Rueckert · 2017
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Imagination-augmented agents for deep reinforcement learning
Sébastien Racanière, Théophane Weber, David Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adrià Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, et al · 2017
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Deep multimodal learning: A survey on recent advances and trends
Dhanesh Ramachandram and Graham W Taylor · 2017
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Learning to compose domain-specific transformations for data augmentation
Alexander J Ratner, Henry Ehrenberg, Zeshan Hussain, Jared Dunnmon, and Christopher Ré · 2017
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, and Christoph H Lampert · 2017
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Machines that learn with limited or no supervision: A survey on deep learning based techniques
Soumava Roy, Samitha Herath, Richard Nock, and Fatih Porikli · 2017
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Sim-to-real robot learning from pixels with progressive nets
Andrei A Rusu, Matej Večerík, Thomas Rothörl, Nicolas Heess, Razvan Pascanu, and Raia Hadsell · 2017
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Deep convolutional neural networks and data augmentation for environmental sound classification
Justin Salamon and Juan Pablo Bello · 2017
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Training neural networks with very little data–a draft
Hojjat Salehinejad, Joseph Barfett, Shahrokh Valaee, and Timothy Dowdell · 2017
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A simple neural network module for relational reasoning
Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Tim Lillicrap · 2017
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One-shot learning for semantic segmentation
Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, and Byron Boots · 2017
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Incremental learning of object detectors without catastrophic forgetting
Konstantin Shmelkov, Cordelia Schmid, and Karteek Alahari · 2017
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Learning from simulated and unsupervised images through adversarial training
Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda Wang, and Russ Webb · 2017
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Machine learning with world knowledge: The position and survey
Yangqiu Song and Dan Roth · 2017
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Label-free supervision of neural networks with physics and domain knowledge
Russell Stewart and Stefano Ermon · 2017
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Siamese networks for chromosome classification
Swati, Gaurav Gupta, Mohit Yadav, Monika Sharma, Lovekesh Vig, et al · 2017
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Unsupervised cross-domain image generation
Yaniv Taigman, Adam Polyak, and Lior Wolf · 2017
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Towards a unified compositional model for visual pattern modeling
Wei Tang, Pei Yu, Jiahuan Zhou, and Ying Wu · 2017
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Few-shot learning through an information retrieval lens
Eleni Triantafillou, Richard Zemel, and Raquel Urtasun · 2017
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Improving one-shot learning through fusing side information
Yao-Hung Hubert Tsai and Ruslan Salakhutdinov · 2017
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Learning robust visual-semantic embeddings
Yao-Hung Hubert Tsai, Liang-Kang Huang, and Ruslan Salakhutdinov · 2017
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Flipper: A systematic approach to debugging training sets
Paroma Varma, Dan Iter, Christopher De Sa, and Christopher Ré · 2017
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Deep-learning systems for domain adaptation in computer vision: Learning transferable feature representations
Hemanth Venkateswara, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
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A simple exponential family framework for zero-shot learning
Vinay Kumar Verma and Piyush Rai · 2017
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