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This work explores the use of spatial context as a source of free and plentiful supervisory signal for training a rich visual representation.
Learning invariance from transformation sequences
P. Földiák · 1991
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The “wake-sleep” algorithm for unsupervised neural networks
G. E. Hinton, P. Dayan, B. J. Frey, and R. M. Neal · 1995
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Emergence of simple-cell receptive field properties by learning a sparse code for natural images
B. A. Olshausen and D. J. Field · 1996
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On seeing stuff: the perception of materials by humans and machines
E. H. Adelson · 2001
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Slow feature analysis:unsupervised learning of invariances
L. Wiskott and T. J. Sejnowski · 2002
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A framework for learning predictive structures from multiple tasks and unlabeled data
R. K. Ando and T. Zhang · 2005
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Discovering objects and their location in images
J. Sivic, B. C. Russell, A. A. Efros, A. Zisserman, and W. T. Freeman · 2005
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Unsupervised learning of categories from sets of partially matching image features
K. Grauman and T. Darrell · 2006
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A fast learning algorithm for deep belief nets
G. Hinton, S. Osindero, and Y.-W. Teh · 2006
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Efficient sparse coding algorithms
H. Lee, A. Battle, R. Raina, and A. Y. Ng · 2006
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Using multiple segmentations to discover objects and their extent in image collections
B. C. Russell, W. T. Freeman, A. A. Efros, J. Sivic, and A. Zisserman · 2006
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Total recall: Automatic query expansion with a generative feature model for object retrieval
O. Chum, J. Philbin, J. Sivic, M. Isard, and A. Zisserman · 2007
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A discriminative language model with pseudo-negative samples
D. Okanohara and J. Tsujii · 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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Who killed the directed model?
J. Domke, A. Karapurkar, and Y. Aloimonos · 2008
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Unsupervised modeling of object categories using link analysis techniques
G. Kim, C. Faloutsos, and M. Hebert · 2008
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World-scale mining of objects and events from community photo collections
T. Quack, B. Leibe, and L. Van Gool · 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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Geometric min-hashing: Finding a (thick) needle in a haystack
O. Chum, M. Perdoch, and J. Matas · 2009
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Foreground focus: Unsupervised learning from partially matching images
Y. J. Lee and K. Grauman · 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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Deep boltzmann machines
R. Salakhutdinov and G. E. Hinton · 2009
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The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
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Object detection with discriminatively trained part-based models
P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan · 2010
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Image webs: Computing and exploiting connectivity in image collections
K. Heath, N. Gelfand, M. Ovsjanikov, M. Aanjaneya, and L. J. Guibas · 2010
Cited alongside, same era.
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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Learning discriminative part detectors for image classification and cosegmentation
J. Sun and J. Ponce · 2013
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Regionlets for generic object detection
X. Wang, M. Yang, S. Zhu, and Y. Lin · 2013
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Analyzing the performance of multilayer neural networks for object recognition
P. Agrawal, R. Girshick, and J. Malik · 2014
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Deep generative stochastic networks trainable by backprop
Y. Bengio, E. Thibodeau-Laufer, G. Alain, and J. Yosinski · 2014
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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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From a set of shapes to object discovery
N. Payet and S. Todorovic · 2010
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The neural autoregressive distribution estimator
H. Larochelle and I. Murray · 2011
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Unbiased look at dataset bias
A. Torralba and A. A. Efros · 2011
Cited alongside, same era.
What makes Paris look like Paris?
C. Doersch, S. Singh, A. Gupta, J. Sivic, and A. A. Efros · 2012
Cited alongside, same era.
“clustering by composition”–unsupervised discovery of image categories
A. Faktor and M. Irani · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
Cited alongside, same era.
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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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Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. 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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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2014
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Stochastic backpropagation and approximate inference in deep generative models
D. J. Rezende, S. Mohamed, and D. Wierstra · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Fast r-cnn
R. Girshick · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Data-dependent initializations of convolutional neural networks
P. Krähenbühl, C. Doersch, J. Donahue, and T. Darrell · 2015
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Dataset fingerprints: Exploring image collections through data mining
K. Rematas, B. Fernando, F. Dellaert, and T. Tuytelaars · 2015
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Generative image modeling using spatial lstms
L. Theis and M. Bethge · 2015
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The new data and new challenges in multimedia research
B. Thomee, D. A. Shamma, G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and L.-J. Li · 2015
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Unsupervised learning of visual representations using videos
X. Wang and A. Gupta · 2015
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