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Clustering is a class of unsupervised learning methods that has been extensively applied and studied in computer vision.
Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: · 1958
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Towards an organizing principle for a layered perceptual network
Linsker, R.: · 1988
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Face recognition using eigenfaces
Turk, M.A., Pentland, A.P.: · 1991
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Learning classification with unlabeled data
de Sa, V.R.: · 1994
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: · 1998
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Normalized cuts and image segmentation
Shi, J., Malik, J.: · 2000
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The elements of statistical learning. Volume 1
Friedman, J., Hastie, T., Tibshirani, R.: · 2001
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Visual categorization with bags of keypoints
Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: · 2004
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Maximum margin clustering
Xu, L., Neufeld, J., Larson, B., Schuurmans, D.: · 2005
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Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: · 2007
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Unsupervised learning of invariant feature hierarchies with applications to object recognition
Huang, F.J., Boureau, Y.L., LeCun, Y., et al.: · 2007
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Object retrieval with large vocabularies and fast spatial matching
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: · 2007
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Diffrac: a discriminative and flexible framework for clustering
Bach, F.R., Harchaoui, Z.: · 2008
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Lost in quantization: Improving particular object retrieval in large scale image databases
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: · 2008
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: · 2009
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Visualizing higher-layer features of a deep network
Erhan, D., Bengio, Y., Courville, A., Vincent, P.: · 2009
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Discriminative clustering for image co-segmentation
Joulin, A., Bach, F., Ponce, J.: · 2010
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Power iteration clustering
Lin, F., Cohen, W.W.: · 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., Manzagol, P.A.: · 2010
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Evaluating color descriptors for object and scene recognition
Van De Sande, K., Gevers, T., Snoek, C.: · 2010
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Ensemble of exemplar-svms for object detection and beyond
Malisiewicz, T., Gupta, A., Efros, A.A.: · 2011
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Stacked convolutional auto-encoders for hierarchical feature extraction
Masci, J., Meier, U., Cireşan, D., Schmidhuber, J.: · 2011
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Learning feature representations with k-means
Coates, A., Ng, A.Y.: · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Stochastic gradient descent tricks
Bottou, L.: · 2012
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A convex relaxation for weakly supervised classifiers
Joulin, A., Bach, F.: · 2012
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Mode-seeking on graphs via random walks
Cho, M., Lee, K.M.: · 2012
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Deepflow: Large displacement optical flow with deep matching
Weinzaepfel, P., Revaud, J., Harchaoui, Z., Schmid, C.: · 2013
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Auto-encoding variational bayes
Kingma, D.P., Welling, M.: · 2013
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Cnn features off-the-shelf: an astounding baseline for recognition
Sharif Razavian, A., Azizpour, H., Sullivan, J., Carlsson, S.: · 2014
Cited alongside, same era.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Human pose estimation with iterative error feedback
Carreira, J., Agrawal, P., Fragkiadaki, K., Malik, J.: · 2016
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Densely connected convolutional networks
Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: · 2016
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Crowdsourcing in computer vision
Kovashka, A., Russakovsky, O., Fei-Fei, L., Grauman, K., et al.: · 2016
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Seeing through the human reporting bias: Visual classifiers from noisy human-centric labels
Misra, I., Zitnick, C.L., Mitchell, M., Girshick, R.: · 2016
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Donahue, J., Krähenbühl, P., Darrell, T.: · 2016
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Simonyan, K., Zisserman, A.: · 2014
Cited alongside, same era.
Discriminative unsupervised feature learning with convolutional neural networks
Dosovitskiy, A., Springenberg, J.T., Riedmiller, M., Brox, T.: · 2014
Cited alongside, same era.
Convolutional kernel networks
Mairal, J., Koniusz, P., Harchaoui, Z., Schmid, C.: · 2014
Cited alongside, same era.
Visualizing and understanding convolutional networks
Zeiler, M.D., Fergus, R.: · 2014
Cited alongside, same era.
Learning deep features for scene recognition using places database
Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: · 2014
Cited alongside, same era.
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: · 2014
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2015
Cited alongside, same era.
Yang, J., Parikh, D., Batra, D.: · 2016
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Unsupervised deep embedding for clustering analysis
Xie, J., Girshick, R., Farhadi, A.: · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Noroozi, M., Favaro, P.: · 2016
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Colorful image colorization
Zhang, R., Isola, P., Efros, A.A.: · 2016
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Learning deep parsimonious representations
Liao, R., Schwing, A., Zemel, R., Urtasun, R.: · 2016
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Context encoders: Feature learning by inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: · 2016
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Learning representations for automatic colorization
Larsson, G., Maire, M., Shakhnarovich, G.: · 2016
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Split-brain autoencoders: Unsupervised learning by cross-channel prediction
Zhang, R., Isola, P., Efros, A.A.: · 2016
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Ambient sound provides supervision for visual learning
Owens, A., Wu, J., McDermott, J.H., Freeman, W.T., Torralba, A.: · 2016
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Adversarially learned inference
Dumoulin, V., Belghazi, I., Poole, B., Lamb, A., Arjovsky, M., Mastropietro, O., Courville, A.: · 2016
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Learning visual features from large weakly supervised data
Joulin, A., van der Maaten, L., Jabri, A., Vasilache, N.: · 2016
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Stock, P., Cisse, M.: · 2017
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Unsupervised learning by predicting noise
Bojanowski, P., Joulin, A.: · 2017
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Learning features by watching objects move
Pathak, D., Girshick, R., Dollár, P., Darrell, T., Hariharan, B.: · 2017
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Representation learning by learning to count
Noroozi, M., Pirsiavash, H., Favaro, P.: · 2017
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Transitive invariance for self-supervised visual representation learning
Wang, X., He, K., Gupta, A.: · 2017
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Multi-task self-supervised visual learning
Doersch, C., Zisserman, A.: · 2017
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Optimizing the latent space of generative networks
Bojanowski, P., Joulin, A., Lopez-Paz, D., Szlam, A.: · 2017
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Billion-scale similarity search with gpus
Johnson, J., Douze, M., Jégou, H.: · 2017
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An evaluation of large-scale methods for image instance and class discovery
Douze, M., Jégou, H., Johnson, J.: · 2017
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