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While deep neural networks have been shown to perform remarkably well in many machine learning tasks, labeling a large amount of ground truth data for supervised training is usually very costly to scale.
P. Burt and E. Adelson, “The laplacian pyramid as a compact image code,” IEEE Transactions on communications , vol. 31, no. 4, pp. 532–540, 1983
1983
Earlier work this paper cites.
G. E. Hinton and T. J. Sejnowski, “Learning and releaming in boltzmann machines,” Parallel distributed processing: Explorations in the microstructure of cognition , vol. 1, no. 282-317, p. 2, 1986
1986
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner et al. , “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” science , vol. 313, no. 5786, pp. 504–507, 2006
2006
Earlier work this paper cites.
Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, “Greedy layer-wise training of deep networks,” in Advances in neural information processing systems (NeurIPS) , 2007
2007
Earlier work this paper cites.
C. Poultney, S. Chopra, Y. L. Cun et al. , “Efficient learning of sparse representations with an energy-based model,” in Advances in neural information processing systems (NeurIPS) , 2007
2007
Earlier work this paper cites.
L. v. d. Maaten and G. Hinton, “Visualizing data using t-sne,” Journal of machine learning research , vol. 9, no. Nov, pp. 2579–2605, 2008
2008
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2009
2009
Earlier work this paper cites.
A. Krizhevsky, “Learning multiple layers of features from tiny images,” Citeseer, Tech. Rep., 2009
2009
Earlier work this paper cites.
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,” Journal of Machine Learning Research , vol. 11, no. Dec, pp. 3371–3408, 2010
2010
Earlier work this paper cites.
A. Bubić, D. Y. von Cramon, and R. I. Schubotz, “Prediction, cognition and the brain,” Frontiers in Human Neuroscience , vol. 4, no. 25, 2010
2010
Earlier work this paper cites.
R. Salakhutdinov and H. Larochelle, “Efficient learning of deep boltzmann machines,” in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics , 2010
2010
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (voc) challenge,” International journal of computer vision , vol. 88, no. 2, pp. 303–338, 2010
2010
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems (NeurIPS) , 2012
2012
Earlier work this paper cites.
Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 35, no. 8, pp. 1798–1828, 2013
2013
Cited alongside, same era.
2013
Cited alongside, same era.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems (NeurIPS) , 2014
2014
Cited alongside, same era.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in Proceedings of European Conference on Computer Vision (ECCV) , 2014
2014
Cited alongside, same era.
M. Noroozi and P. Favaro, “Unsupervised learning of visual representations by solving jigsaw puzzles,” in Proceedings of European Conference on Computer Vision (ECCV) , 2016
2016
Later among the works it cites.
2016
Later among the works it cites.
I. Misra, C. L. Zitnick, and M. Hebert, “Shuffle and learn: unsupervised learning using temporal order verification,” in Proceedings of European Conference on Computer Vision (ECCV) , 2016
2016
Later among the works it cites.
——, “Split-brain autoencoders: Unsupervised learning by cross-channel prediction,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017
2017
Later among the works it cites.
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2014
Cited alongside, same era.
B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva, “Learning deep features for scene recognition using places database,” in Advances in neural information processing systems (NeurIPS) , 2014
2014
Cited alongside, same era.
C. Doersch, A. Gupta, and A. A. Efros, “Unsupervised visual representation learning by context prediction,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) , 2015
2015
Cited alongside, same era.
R. Girshick, “Fast r-cnn,” in Proceedings of the IEEE international conference on computer vision (ICCV) , 2015
2015
Cited alongside, same era.
X. Wang and A. Gupta, “Unsupervised learning of visual representations using videos,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) , 2015
2015
Cited alongside, same era.
2015
Cited alongside, same era.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2015
2015
Cited alongside, same era.
R. Zhang, P. Isola, and A. A. Efros, “Colorful image colorization,” in Proceedings of European Conference on Computer Vision (ECCV) , 2016
2016
Cited alongside, same era.
M. Noroozi, H. Pirsiavash, and P. Favaro, “Representation learning by learning to count,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) , 2017
2017
Later among the works it cites.
D. Pathak, R. Girshick, P. Dollár, T. Darrell, and B. Hariharan, “Learning features by watching objects move,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017
2017
Later among the works it cites.
2018
Later among the works it cites.
X. Wang, R. Girshick, A. Gupta, and K. He, “Non-local neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018
2018
Later among the works it cites.
Z. Ren and Y. J. Lee, “Cross-domain self-supervised multi-task feature learning using synthetic imagery,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018
2018
Later among the works it cites.
Z. Wu, Y. Xiong, S. X. Yu, and D. Lin, “Unsupervised feature learning via non-parametric instance discrimination,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018
2018
Later among the works it cites.
H. Xu, X. Lv, X. Wang, Z. Ren, N. Bodla, and R. Chellappa, “Deep regionlets: Blended representation and deep learning for generic object detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2019
2019
Later among the works it cites.
L. Zhang, G.-J. Qi, L. Wang, and J. Luo, “Aet vs. aed: Unsupervised representation learning by auto-encoding transformations rather than data,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019
2019
Later among the works it cites.
J. Huang, Q. Dong, S. Gong, and X. Zhu, “Unsupervised deep learning by neighbourhood discovery,” in International Conference on Machine Learning (ICML) , 2019
2019
Later among the works it cites.