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Modern compression algorithms are often the result of laborious domain-specific research; industry standards such as MP3, JPEG, and AMR-WB took years to develop and were largely hand-designed.
“Neural networks for vector quantization of speech and images,”
Ashok K. Krishnamurthy, Stanley C. Ahalt, Douglas E. Melton, and Prakoon Chen, · 1990
Earlier work this paper cites.
“Speech coding based on a multi-layer neural network,”
Shigeo Morishima, H Harashima, and Y Katayama, · 1990
Earlier work this paper cites.
“Timit acoustic-phonetic continuous speech corpus,”
John S Garofolo, Lori F Lamel, William M Fisher, Jonathan G Fiscus, David S Pallett, Nancy L Dahlgren, and Victor Zue, · 1993
Earlier work this paper cites.
“Fully vector-quantized neural network-based code-excited nonlinear predictive speech coding,”
Lizhong Wu, Mahesan Niranjan, and Frank Fallside, · 1994
Earlier work this paper cites.
“Neural network approaches to image compression,”
Robert D Dony and Simon Haykin, · 1995
Earlier work this paper cites.
“Image compression with neural networks–a survey,”
J Jiang, · 1999
Earlier work this paper cites.
“The adaptive multirate wideband speech codec (amr-wb),”
Bruno Bessette, Redwan Salami, Roch Lefebvre, Milan Jelinek, Jani Rotola-Pukkila, Janne Vainio, Hannu Mikkola, and Kari Jarvinen, · 2002
Earlier work this paper cites.
Lindasalwa Muda, Mumtaj Begam, and Irraivan Elamvazuthi, · 2010
Cited alongside, same era.
“Deep learning,”
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, · 2015
Cited alongside, same era.
“Phonological vocoding using artificial neural networks,”
Milos Cernak, Blaise Potard, and Philip N Garner, · 2015
Cited alongside, same era.
“Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2015
Cited alongside, same era.
“Deep multi-scale video prediction beyond mean square error,”
Michael Mathieu, Camille Couprie, and Yann LeCun, · 2015
Cited alongside, same era.
“Deep residual learning for image recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2016
Later among the works it cites.
“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
Later among the works it cites.
“Generating images with perceptual similarity metrics based on deep networks,”
Alexey Dosovitskiy and Thomas Brox, · 2016
Later among the works it cites.
“Sgdr: stochastic gradient descent with restarts,”
Ilya Loshchilov and Frank Hutter, · 2016
Later among the works it cites.
“Soft-to-hard vector quantization for end-to-end learned compression of images and neural networks,”
Eirikur Agustsson, Fabian Mentzer, Michael Tschannen, Lukas Cavigelli, Radu Timofte, Luca Benini, and Luc Van Gool, · 2017
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“Composition of deep and spiking neural networks for very low bit rate speech coding,”
Milos Cernak, Alexandros Lazaridis, Afsaneh Asaei, and Philip N Garner, · 2016
Cited alongside, same era.
“Full resolution image compression with recurrent neural networks,”
George Toderici, Damien Vincent, Nick Johnston, Sung Jin Hwang, David Minnen, Joel Shor, and Michele Covell, · 2016
Cited alongside, same era.
Closest in time.
Xavier Gastaldi, · 2017
Closest in time.