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Convolutional neural networks provide visual features that perform remarkably well in many computer vision applications.
The hungarian method for the assignment problem
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Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
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Self-organized formation of topologically correct feature maps
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Least squares quantization in pcm
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Handwritten digit recognition with a back-propagation network
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Maximum margin clustering
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Histograms of oriented gradients for human detection
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Diffrac: a discriminative and flexible framework for clustering
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Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., and Larochelle, H · 2007
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Unsupervised learning of invariant feature hierarchies with applications to object recognition
Ranzato, M. A., Huang, F. J., Boureau, Y. L., and LeCun, Y · 2007
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Imagenet: A large-scale hierarchical image database
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The PASCAL visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., and Zisserman, A · 2010
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Discriminative clustering for image co-segmentation
Joulin, A., Bach, F., and Ponce, J · 2010
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Discriminative clustering by regularized information maximization
Krause, A., Perona, P., and Gomes, R. G · 2010
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A · 2010
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Stacked convolutional auto-encoders for hierarchical feature extraction
Masci, J., Meier, U., Cireşan, D., and Schmidhuber, J · 2011
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Learning feature representations with k-means
Coates, A. and Ng, A · 2012
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A convex relaxation for weakly supervised classifiers
Joulin, A. and Bach, F · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G · 2012
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Finding actors and actions in movies
Bojanowski, P., Bach, F., Laptev, I., Ponce, J., Schmid, C., and Sivic, J · 2013
Unsupervised visual representation learning by context prediction
Doersch, C., Gupta, A., and Efros, A · 2015
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Fast r-cnn
Girshick, R · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Learning image representations tied to ego-motion
Jayaraman, D. and Grauman, K · 2015
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Data-dependent initializations of convolutional neural networks
Krähenbühl, Philipp, Doersch, Carl, Donahue, Jeff, and Darrell, Trevor · 2015
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Learning to segment object candidates
Pinheiro, P. O., Collobert, R., and Dollar, P · 2015
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Auto-encoding variational bayes
Kingma, D. and Welling, M · 2013
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Efficient estimation of word representations in vector space
Mikolov, T., Chen, K., Corrado, G., and Dean, J · 2013
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Image classification with the fisher vector: Theory and practice
Sánchez, J., Perronnin, F., Mensink, T., and Verbeek, J · 2013
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Weakly supervised action labeling in videos under ordering constraints
Bojanowski, P., Lajugie, R., Bach, F., Laptev, I., Ponce, J., Schmid, C., and Sivic, J · 2014
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Discriminative unsupervised feature learning with convolutional neural networks
Dosovitskiy, A., Springenberg, J., Riedmiller, M., and Brox, T · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Unsupervised learning of visual representations using videos
Wang, X. and Gupta, A · 2015
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Donahue, J., Krähenbühl, P., and Darrell, T · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Learning visual features from large weakly supervised data
Joulin, A., van der Maaten, L., Jabri, A., and Vasilache, N · 2016
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Learning deep parsimonious representations
Liao, R., Schwing, A., Zemel, R., and Urtasun, R · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Noroozi, M. and Favaro, P · 2016
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Context encoders: Feature learning by inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., and Efros, A · 2016
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Conditional image generation with pixelcnn decoders
van den Oord, A., Kalchbrenner, N., Espeholt, L., Vinyals, O., and Graves, A · 2016
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Unsupervised deep embedding for clustering analysis
Xie, J., Girshick, R., and Farhadi, A · 2016
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Joint unsupervised learning of deep representations and image clusters
Yang, J., Parikh, D., and Batra, D · 2016
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Colorful image colorization
Zhang, R., Isola, P., and Efros, A · 2016
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Stacked What-Where Auto-encoders
Zhao, J., Mathieu, M., Goroshin, R., and LeCun, Y · 2016
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Scale-invariant learning and convolutional networks
Tygert, M., Chintala, S., Szlam, A., Tian, Y., and Zaremba, W · 2017
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