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We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction.
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
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Backpropagation applied to handwritten zip code recognition
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Texture synthesis by non-parametric sampling
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Image inpainting
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An iterative regularization method for total variation-based image restoration
S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin · 2005
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Reducing the dimensionality of data with neural networks
G. E. Hinton and R. R. Salakhutdinov · 2006
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Building the gist of a scene: The role of global image features in recognition
A. Oliva and A. Torralba · 2006
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Scene completion using millions of photographs
J. Hays and A. A. Efros · 2007
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A unified architecture for natural language processing: Deep neural networks with multitask learning
R. Collobert and J. Weston · 2008
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Extracting and composing robust features with denoising autoencoders
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol · 2008
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Patchmatch: A randomized correspondence algorithm for structural image editing
C. Barnes, E. Shechtman, A. Finkelstein, and D. Goldman · 2009
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Learning deep architectures for ai
Y. Bengio · 2009
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Beyond categories: The visual memex model for reasoning about object relationships
T. Malisiewicz and A. Efros · 2009
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Semantic contours from inverse detectors
B. Hariharan, P. Arbeláez, L. Bourdev, S. Maji, and J. Malik · 2011
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What makes paris look like paris?
C. Doersch, S. Singh, A. Gupta, J. Sivic, and A. Efros · 2012
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ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Disentangling factors of variation for facial expression recognition
S. Rifai, Y. Bengio, A. Courville, P. Vincent, and M. Mirza · 2012
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Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean · 2013
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Modeling natural images using gated mrfs
M. Ranzato, V. Mnih, J. M. Susskind, and G. E. Hinton · 2013
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Context as supervisory signal: Discovering objects with predictable context
C. Doersch, A. Gupta, and A. A. Efros · 2014
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Learning to see by moving
P. Agrawal, J. Carreira, and J. Malik · 2015
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Unsupervised visual representation learning by context prediction
C. Doersch, A. Gupta, and A. A. Efros · 2015
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Learning to generate chairs with convolutional neural networks
A. Dosovitskiy, J. T. Springenberg, and T. Brox · 2015
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Fast r-cnn
R. Girshick · 2015
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Unsupervised learning of spatiotemporally coherent metrics
R. Goroshin, J. Bruna, J. Tompson, D. Eigen, and Y. LeCun · 2015
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Learning image representations tied to ego-motion
D. Jayaraman and K. Grauman · 2015
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Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2014
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The Pascal Visual Object Classes challenge: A retrospective
M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. B. Girshick, S. Guadarrama, and T. Darrell · 2014
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2014
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D. Kingma and J. Ba · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Learning temporal embeddings for complex video analysis
V. Ramanathan, K. Tang, G. Mori, and L. Fei-Fei · 2015
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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Unsupervised learning of visual representations using videos
X. Wang and A. Gupta · 2015
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Data-dependent initializations of convolutional neural networks
P. Krähenbühl, C. Doersch, J. Donahue, and T. Darrell · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2016
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