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We present a new approach to learn compressible representations in deep architectures with an end-to-end training strategy.
Arithmetic coding for data compression
Ian H. Witten, Radford M. Neal, and John G. Cleary · 1987
Earlier work this paper cites.
Vector quantization by deterministic annealing
Kenneth Rose, Eitan Gurewitz, and Geoffrey C Fox · 1992
Earlier work this paper cites.
The JPEG still picture compression standard
Gregory K Wallace · 1992
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Competitive learning and soft competition for vector quantizer design
Eyal Yair, Kenneth Zeger, and Allen Gersho · 1992
Earlier work this paper cites.
http://r0k.us/graphics/kodak/ , 1999
Kodak PhotoCD dataset · 1999
Earlier work this paper cites.
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
D. Martin, C. Fowlkes, D. Tal, and J. Malik · 2001
Earlier work this paper cites.
JPEG 2000: Image Compression Fundamentals, Standards and Practice
David S. Taubman and Michael W. Marcellin · 2001
Earlier work this paper cites.
Context-based adaptive binary arithmetic coding in the h. 264/avc video compression standard
Detlev Marpe, Heiko Schwarz, and Thomas Wiegand · 2003
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Multiscale structural similarity for image quality assessment
Z. Wang, E. P. Simoncelli, and A. C. Bovik · 2003
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli · 2004
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Using very deep autoencoders for content-based image retrieval
Alex Krizhevsky and Geoffrey E Hinton · 2011
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Numerical continuation methods: an introduction
Eugene L Allgower and Kurt Georg · 2012
Earlier work this paper cites.
Elements of information theory
Thomas M Cover and Joy A Thomas · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Paul Wohlhart, Martin Kostinger, Michael Donoser, Peter M. Roth, and Horst Bischof · 2013
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Bellard Fabrice · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
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Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
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Yoojin Choi, Mostafa El-Khamy, and Jungwon Lee · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Quantized neural networks: Training neural networks with low precision weights and activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
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Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang · 2016
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Learning both weights and connections for efficient neural network
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Single image super-resolution from transformed self-exemplars
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution
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Variable rate image compression with recurrent neural networks
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Is the deconvolution layer the same as a convolutional layer?
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Full resolution image compression with recurrent neural networks
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Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
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Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun · 2017
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Lossy image compression with compressive autoencoders
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