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Learning the representation and the similarity metric in an end-to-end fashion with deep networks have demonstrated outstanding results for clustering and retrieval.
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Support vector machine learning for interdependent and structured output spaces
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Learning a similarity metric discriminatively, with application to face verification
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Dimensionality reduction by learning an invariant mapping
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Distance metric learning for large margin nearest neighbor classification
K. Q. Weinberger, J. Blitzer, and L. K. Saul · 2006
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Introduction to Information Retrieval
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Cutting-plane training of structural svms
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Submodular meets spectral: Greedy algorithms for subset selection, sparse approximation and dictionary selection
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Product quantization for nearest neighbor search
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The caltech-ucsd birds-200-2011 dataset
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The big data bootstrap
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Submodular function maximization
A. Krause and D. Golovin · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
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Learning mixtures of submodular shells with application to document summarization
H. Lin and J. Bilmes · 2012
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Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude
T. Tieleman and G. Hinton · 2012
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Extreme multi class classification
A. Choromanska, A. Agarwal, and J. Langford · 2013
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3d object representations for fine-grained categorization
J. Krause, M. Stark, J. Deng, and F.-F. Li · 2013
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Pd-sparse: A primal and dual sparse approach to extreme multiclass and multilabel classification
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Training deep networks with structured layers by matrix backpropagation
C. Ionescu, O. Vantzos, and C. Sminchisescu · 2015
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Lazier than lazy greedy
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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, A. C. Berg, and L. Fei-Fei · 2015
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Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 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, and A. Rabinovich · 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, and A. Rabinovich · 2015
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Fastxml: A fast, accurate and stable tree-classifier for extreme multi-label learning
Y. Prabhu and M. Varma · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Learning mixtures of submodular functions for image collection summarization
S. Tschiatschek, R. Iyer, H. Wei, and J. Bilmes · 2014
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Accelerating t-sne using tree-based algorithms
L. van der maaten · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2015
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Learning visual similarity for product design with convolutional neural networks
S. Bell and K. Bala · 2015
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Unsupervised learning of visual representations using videos
X. Wang and A. Gupta · 2015
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Universal correspondence network
C. B. Choy, J. Gwak, S. Savarese, and M. Chandraker · 2016
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Deep clustering: Discriminative embeddings for segmentation and separation
J. R. Hershey, Z. Chen, J. L. Roux, and S. Watanabe · 2016
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Learning transferrable representations for unsupervised domain adaptation
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Improved deep metric learning with multi-class n-pair loss objective
K. Sohn · 2016
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Deep metric learning via lifted structured feature embedding
H. O. Song, Y. Xiang, S. Jegelka, and S. Savarese · 2016
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Improving the robustness of deep neural networks via stability training
S. Zheng, Y. Song, T. Leung, and I. Goodfellow · 2016
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