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Learning visual features from unlabeled image data is an important yet challenging task, which is often achieved by training a model on some annotation-free information.
Unsupervised learning of image manifolds by semidefinite programming
K. Q. Weinberger and L. K. Saul · 2006
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Caltech-256 object category dataset
G. Griffin, A. Holub, and P. Perona · 2007
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Patch-based within-object classification
J. Aghajanian, J. Warrell, S. J. Prince, P. Li, J. L. Rohn, and B. Baum · 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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What does classifying more than 10,000 image categories tell us?
J. Deng, A. C. Berg, K. Li, and L. Fei-Fei · 2010
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Why does unsupervised pre-training help deep learning?
D. Erhan, Y. Bengio, A. Courville, P. A. Manzagol, P. Vincent, and S. Bengio · 2010
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The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2010
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A supervised patch-based approach for human brain labeling
F. Rousseau, P. A. Habas, and C. Studholme · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Building high-level features using large scale unsupervised learning
Q. V. Le · 2013
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Fixed-point model for structured labeling
Q. Li, J. Wang, D. Wipf, and Z. Tu · 2013
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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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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Large-scale video classification with convolutional neural networks
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei · 2014
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Cnn features off-the-shelf: an astounding baseline for recognition
A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson · 2014
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Unsupervised visual representation learning by context prediction
C. Doersch, A. Gupta, and A. A. Efros · 2015
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Fast r-cnn
R. Girshick · 2015
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Deep learning
Y. LeCun, Y. Bengio, and G. E. Hinton · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Weakly- and semi-supervised learning of a dcnn for semantic image segmentation
G. Papandreou, L. C. Chen, K. Murphy, and A. L. Yuille · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
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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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Joint unsupervised learning of deep representations and image clusters
J. Yang, D. Parikh, and D. Batra · 2016
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Densely connected convolutional networks
G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 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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Representation learning by learning to count
M. Noroozi, H. Pirsiavash, and P. Favaro · 2017
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Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation
H. R. Roth, L. Lu, A. Farag, H. Shin, J. Liu, E. B. Turkbey, and R. M. Summers · 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, et al · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, et al · 2015
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Unsupervised learning of visual representations using videos
X. Wang and A. Gupta · 2015
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3d u-net: learning dense volumetric segmentation from sparse annotation
O. Cicek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger · 2016
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Unpaired image-to-image translation using cycle-consistent adversarial networks
J. Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
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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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Unsupervised representation learning by predicting image rotations
S. Gidaris, P. Singh, and N. Komodakis · 2018
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Improvements to context based self-supervised learning
T. N. Mundhenk, D. Ho, and B. Y. Chen · 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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Deep co-training for semi-supervised image recognition
S. Qiao, W. Shen, Z. Zhang, B. Wang, and A. L. Yuille · 2018
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Cross-domain self-supervised multi-task feature learning using synthetic imagery
Z. Ren and Y. J. Lee · 2018
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Bridging the gap between 2d and 3d organ segmentation with volumetric fusion net
Y. Xia, L. Xie, F. Liu, Z. Zhu, E. K. Fishman, and A. L. Yuille · 2018
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Recurrent saliency transformation network: Incorporating multi-stage visual cues for small organ segmentation
Q. Yu, L. Xie, Y. Wang, Y. Zhou, E. K. Fishman, and A. L. Yuille · 2018
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Deepvoting: An explainable framework for semantic part detection under partial occlusion
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