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Self-supervised learning aims to learn representations from the data itself without explicit manual supervision.
Backpropagation applied to handwritten zip code recognition
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A training algorithm for optimal margin classifiers
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Learning classification with unlabeled data
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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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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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Dimensionality reduction by learning an invariant mapping
R. Hadsell, S. Chopra, and Y. LeCun · 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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Unsupervised learning of invariant feature hierarchies with applications to object recognition
F. J. Huang, Y.-L. Boureau, Y. LeCun, et al · 2007
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LIBLINEAR: A library for large linear classification
R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin · 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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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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Deep learning from temporal coherence in video
H. Mobahi, R. Collobert, and J. Weston · 2009
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Deep boltzmann machines
R. Salakhutdinov and G. 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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Stacked convolutional auto-encoders for hierarchical feature extraction
J. Masci, U. Meier, D. Cireşan, and J. Schmidhuber · 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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Indoor segmentation and support inference from rgbd images
N. Silberman, D. Hoiem, P. Kohli, and R. Fergus · 2012
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Unsupervised discovery of mid-level discriminative patches
S. Singh, A. Gupta, and A. A. Efros · 2012
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Data-driven 3d primitives for single image understanding
D. F. Fouhey, A. Gupta, and M. Hebert · 2013
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Selective search for object recognition
J. R. Uijlings, K. E. Van De Sande, T. Gevers, and A. W. Smeulders · 2013
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, 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. Girshick, S. Guadarrama, and T. Darrell · 2014
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
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Discriminatively trained dense surface normal estimation
L. Ladickỳ, B. Zeisl, and M. Pollefeys · 2014
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Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Learning deep features for scene recognition using places database
B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva · 2014
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Learning to see by moving
P. Agrawal, J. Carreira, and J. Malik · 2015
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Learning large-scale automatic image colorization
A. Deshpande, J. Rock, and D. Forsyth · 2015
Cited alongside, same era.
Unsupervised visual representation learning by context prediction
C. Doersch, A. Gupta, and A. A. Efros · 2015
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Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
D. Eigen and R. Fergus · 2015
Cited alongside, same era.
The pascal visual object classes challenge: A retrospective
M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2015
Cited alongside, same era.
Fast r-cnn
R. Girshick · 2015
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Learning image representations tied to ego-motion
D. Jayaraman and K. Grauman · 2015
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Accurate, large minibatch sgd: Training imagenet in 1 hour
P. Goyal, P. Dollár, R. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, and K. He · 2017
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Colorization as a proxy task for visual understanding
G. Larsson, M. Maire, and G. Shakhnarovich · 2017
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Predicting deeper into the future of semantic segmentation
P. Luc, N. Neverova, C. Couprie, J. Verbeek, and Y. LeCun · 2017
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Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks
L. Mescheder, S. Nowozin, and A. Geiger · 2017
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Representation learning by learning to count
M. Noroozi, H. Pirsiavash, and P. Favaro · 2017
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Learning features by watching objects move
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P. Krähenbühl, C. Doersch, J. Donahue, and T. Darrell · 2015
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Cited alongside, same era.
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
Cited alongside, same era.
Yfcc100m: The new data in multimedia research
B. Thomee, D. A. Shamma, G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and L.-J. Li · 2015
Cited alongside, same era.
Designing deep networks for surface normal estimation
X. Wang, D. Fouhey, and A. Gupta · 2015
Cited alongside, same era.
Unsupervised learning of visual representations using videos
X. Wang and A. Gupta · 2015
Cited alongside, same era.
D. Pathak, R. Girshick, P. Dollár, T. Darrell, and B. Hariharan · 2017
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
C. Sun, A. Shrivastava, S. Singh, and A. Gupta · 2017
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Transitive invariance for self-supervised visual representation learning
X. Wang, K. He, and A. Gupta · 2017
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Split-brain autoencoders: Unsupervised learning by cross-channel prediction
R. Zhang, P. Isola, and A. A. Efros · 2017
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Pyramid scene parsing network
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2017
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Unsupervised learning of depth and ego-motion from video
T. Zhou, M. Brown, N. Snavely, and D. G. Lowe · 2017
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Objects that sound
R. Arandjelovic and A. Zisserman · 2018
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Deep clustering for unsupervised learning of visual features
M. Caron, P. Bojanowski, A. Joulin, and M. Douze · 2018
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Learning to separate object sounds by watching unlabeled video
R. Gao, R. Feris, and K. Grauman · 2018
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Unsupervised representation learning by predicting image rotations
S. Gidaris, P. Singh, and N. Komodakis · 2018
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Detectron, 2018
R. Girshick, I. Radosavovic, G. Gkioxari, P. Dollár, and K. He · 2018
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Exploring the limits of weakly supervised pretraining
D. Mahajan, R. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. van der Maaten · 2018
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Boosting self-supervised learning via knowledge transfer
M. Noroozi, A. Vinjimoor, P. Favaro, and H. Pirsiavash · 2018
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Representation learning with contrastive predictive coding
A. v. d. Oord, Y. Li, and O. Vinyals · 2018
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Megdet: A large mini-batch object detector
C. Peng, T. Xiao, Z. Li, Y. Jiang, X. Zhang, K. Jia, G. Yu, and J. Sun · 2018
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A. Sax, B. Emi, A. R. Zamir, L. Guibas, S. Savarese, and J. Malik · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Z. Wu, Y. Xiong, S. X. Yu, and D. Lin · 2018
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Gibson env: Real-world perception for embodied agents
F. Xia, A. R. Zamir, Z. He, A. Sax, J. Malik, and S. Savarese · 2018
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Semantic understanding of scenes through the ade20k dataset
B. Zhou, H. Zhao, X. Puig, T. Xiao, S. Fidler, A. Barriuso, and A. Torralba · 2018
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https://github.com/CSAILVision/semantic-segmentation-pytorch
CSAILVision Segmentation · 2019
Closest in time.
https://people.eecs.berkeley.edu/˜efros/gelato˙bet.html
The Gelato Bet · 2019
Closest in time.
Revisiting self-supervised visual representation learning
A. Kolesnikov, X. Zhai, and L. Beyer · 2019
Closest in time.