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Neural networks have greatly boosted performance in computer vision by learning powerful representations of input data.
Distributed representations
Geoffrey E. Hinton, James L. McClelland, and David E. Rumelhart · 1986
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Yann LeCun · 1998
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Learning multiple layers of features from tiny images
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Sergey Karayev, Matthew Trentacoste, Helen Han, Aseem Agarwala, Trevor Darrell, Aaron Hertzmann, and Holger Winnemoeller · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Deep neural networks for object detection
Christian Szegedy, Alexander Toshev, and Dumitru Erhan · 2013
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Semantic image segmentation with deep convolutional nets and fully connected crfs, 2014
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille · 2014
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Nice: Non-linear independent components estimation, 2014
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Autoencoding beyond pixels using a learned similarity metric, 2015
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks, 2015
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Deep visual analogy-making
Scott E Reed, Yi Zhang, Yuting Zhang, and Honglak Lee · 2015
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Understanding neural networks through deep visualization, 2015
Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson · 2015
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Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, and Xiaoou Tang · 2016
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Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, and Jeff Clune · 2016
MGAN: Training generative adversarial nets with multiple generators
Quan Hoang, Tu Dinh Nguyen, Trung Le, and Dinh Phung · 2018
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Excessive invariance causes adversarial vulnerability, 2018
Jörn-Henrik Jacobsen, Jens Behrmann, Richard Zemel, and Matthias Bethge · 2018
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i-revnet: Deep invertible networks, 2018
Jörn-Henrik Jacobsen, Arnold Smeulders, and Edouard Oyallon · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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Disentangled person image generation
Liqian Ma, Qianru Sun, Stamatios Georgoulis, Luc Van Gool, Bernt Schiele, and Mario Fritz · 2018
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Umap: Uniform manifold approximation and projection for dimension reduction, 2018
Leland McInnes, John Healy, and James Melville · 2018
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“why should i trust you?”
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Learning deep features for discriminative localization
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Network dissection: Quantifying interpretability of deep visual representations
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger · 2017
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
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Are gans created equal? a large-scale study, 2017
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Latent dirichlet allocation in generative adversarial networks, 2018
Lili Pan, Shen Cheng, Jian Liu, Yazhou Ren, and Zenglin Xu · 2018
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Rise: Randomized input sampling for explanation of black-box models, 2018
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
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Interpretable convolutional neural networks
Quanshi Zhang, Ying Nian Wu, and Song-Chun Zhu · 2018
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Diagnosing and enhancing vae models, 2019
Bin Dai and David Wipf · 2019
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Unsupervised robust disentangling of latent characteristics for image synthesis
Patrick Esser, Johannes Haux, and Bjorn Ommer · 2019
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Ganalyze: Toward visual definitions of cognitive image properties
Lore Goetschalckx, Alex Andonian, Aude Oliva, and Phillip Isola · 2019
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Content and style disentanglement for artistic style transfer
Dmytro Kotovenko, Artsiom Sanakoyeu, Sabine Lang, and Bjorn Ommer · 2019
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Few-shot unsupervised image-to-image translation
Ming-Yu Liu, Xun Huang, Arun Mallya, Tero Karras, Timo Aila, Jaakko Lehtinen, and Jan Kautz · 2019
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Unsupervised part-based disentangling of object shape and appearance
Dominik Lorenz, Leonard Bereska, Timo Milbich, and Bjorn Ommer · 2019
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Explanation in artificial intelligence: Insights from the social sciences
Tim Miller · 2019
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Arbitrary style transfer with style-attentional networks
Dae Young Park and Kwang Hee Lee · 2019
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Interpreting the latent space of gans for semantic face editing
Yujun Shen, Jinjin Gu, Xiaoou Tang, and Bolei Zhou · 2019
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