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We investigate and improve self-supervision as a drop-in replacement for ImageNet pretraining, focusing on automatic colorization as the proxy task.
Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex
J. H. van Hateren and D. L. Ruderman · 1998
Earlier work this paper cites.
On the momentum term in gradient descent learning algorithms
N. Qian · 1999
Earlier work this paper cites.
Simple-cell-like receptive fields maximize temporal coherence in natural video
J. Hurri and A. Hyvärinen · 2003
Earlier work this paper cites.
Semi-supervised learning by entropy minimization
Y. Grandvalet and Y. Bengio · 2004
Earlier work this paper cites.
Deep learning from temporal coherence in video
H. Mobahi, R. Collobert, and J. Weston · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
Earlier work this paper cites.
An analysis of single-layer networks in unsupervised feature learning
A. Coates, H. Lee, and A. Y. Ng · 2011
Earlier work this paper cites.
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
Earlier work this paper cites.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2014
Earlier work this paper cites.
Reconstructive sparse code transfer for contour detection and semantic labeling
M. Maire, S. X. Yu, and P. Perona · 2014
Earlier work this paper cites.
Video (language) modeling: a baseline for generative models of natural videos
M. Ranzato, A. Szlam, J. Bruna, M. Mathieu, R. Collobert, and S. Chopra · 2014
Earlier work this paper cites.
Learning deep features for scene recognition using places database
B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva · 2014
Earlier work this paper cites.
Software available from tensorflow.org
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015 · 2015
Earlier work this paper cites.
Unsupervised visual representation learning by context prediction
C. Doersch, A. Gupta, and A. A. Efros · 2015
Earlier work this paper cites.
Hypercolumns for object segmentation and fine-grained localization
B. Hariharan, P. A. an R. Girshick, and J. Malik · 2015
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 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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Learning visual groups from co-occurrences in space and time
P. Isola, D. Zoran, D. Krishnan, and E. H. Adelson · 2015
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Adam: A method for stochastic optimization
D. P. 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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Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification
S. Iizuka, E. Simo-Serra, and H. Ishikawa · 2016
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Slow and steady feature analysis: higher order temporal coherence in video
D. Jayaraman and K. Grauman · 2016
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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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Learning representations for automatic colorization
G. Larsson, M. Maire, and G. Shakhnarovich · 2016
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Unsupervised learning using sequential verification for action recognition
I. Misra, C. L. Zitnick, and M. Hebert · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
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D. Mishkin and J. Matas · 2015
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Feedforward semantic segmentation with zoom-out features
M. Mostajabi, P. Yadollahpour, and G. Shakhnarovich · 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.
Unsupervised learning of video representations using lstms
N. Srivastava, E. Mansimov, and R. Salakhutdinov · 2015
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Unsupervised learning of visual representations using videos
X. Wang and A. Gupta · 2015
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Learning to segment under various forms of weak supervision
J. Xu, A. G. Schwing, and R. Urtasun · 2015
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Object detectors emerge in deep scene cnns
B. Zhou, A. Khosla, À. Lapedriza, A. Oliva, and A. Torralba · 2015
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M. Noroozi and P. Favaro · 2016
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Ambient sound provides supervision for visual learning
A. Owens, J. Wu, J. H. McDermott, W. T. Freeman, and A. Torralba · 2016
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Learning features by watching objects move
D. Pathak, R. B. Girshick, P. Dollár, T. Darrell, and B. Hariharan · 2016
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Context encoders: Feature learning by inpainting
D. Pathak, P. Krähenbühl, J. Donahue, T. Darrell, and A. Efros · 2016
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
M. Sajjadi, M. Javanmardi, and T. Tasdizen · 2016
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High-performance semantic segmentation using very deep fully convolutional networks
Z. Wu, C. Shen, and A. van den Hengel · 2016
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Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2016
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Colorful image colorization
R. Zhang, P. Isola, and A. A. Efros · 2016
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Adversarial feature learning
J. Donahue, P. Krähenbühl, and T. Darrell · 2017
Closest in time.
Split-brain autoencoders: Unsupervised learning by cross-channel prediction
R. Zhang, P. Isola, and A. A. Efros · 2017
Closest in time.