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Recently, an extensive amount of research has been focused on compressing and accelerating Deep Neural Networks (DNN).
Learning multiple layers of features from tiny images
Alex Krizhevsky et al · 2009
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
Earlier work this paper cites.
Auto-encoding variational bayes, 2013
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Generative adversarial networks, 2014
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Inceptionism: Going deeper into neural networks
Alexander Mordvintsev, Christopher Olah, and Mike Tyka · 2015
Earlier work this paper cites.
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, et al · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Improved techniques for training gans, 2016
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Earlier work this paper cites.
Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium, 2017
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Cited alongside, same era.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger · 2017
Cited alongside, same era.
Quantized neural networks: Training neural networks with low precision weights and activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2017
Cited alongside, same era.
Data-free knowledge distillation for deep neural networks
Raphael Gontijo Lopes, Stefano Fenu, and Thad Starner · 2017
Cited alongside, same era.
Automatic differentiation in PyTorch
Discovering low-precision networks close to full-precision networks for efficient embedded inference, 2018
Jeffrey L. McKinstry, Steven K. Esser, Rathinakumar Appuswamy, Deepika Bablani, John V. Arthur, Izzet B. Yildiz, and Dharmendra S. Modha · 2018
Later among the works it cites.
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Post training 4-bit quantization of convolutional networks for rapid-deployment
Ron Banner, Yury Nahshan, and Daniel Soudry · 2019
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Dream distillation: A data-independent model compression framework
Kartikeya Bhardwaj, Naveen Suda, and Radu Marculescu · 2019
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Augment your batch: better training with larger batches
Elad Hoffer, Tal Ben-Nun, Itay Hubara, Niv Giladi, Torsten Hoefler, and Daniel Soudry · 2019
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Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
Cited alongside, same era.
Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
Cited alongside, same era.
Quantization and training of neural networks for efficient integer-arithmetic-only inference
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko · 2018
Cited alongside, same era.
Quantizing deep convolutional networks for efficient inference: A whitepaper
Raghuraman Krishnamoorthi · 2018
Cited alongside, same era.
Closest in time.
Same, same but different-recovering neural network quantization error through weight factorization
Eldad Meller, Alexander Finkelstein, Uri Almog, and Mark Grobman · 2019
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Data-free quantization through weight equalization and bias correction
Markus Nagel, Mart van Baalen, Tijmen Blankevoort, and Max Welling · 2019
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Zero-shot knowledge distillation in deep networks
Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, R Venkatesh Babu, and Anirban Chakraborty · 2019
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Dreaming to distill: Data-free knowledge transfer via deepinversion, 2019
Hongxu Yin, Pavlo Molchanov, Zhizhong Li, Jose M. Alvarez, Arun Mallya, Derek Hoiem, Niraj K. Jha, and Jan Kautz · 2019
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Zeroq: A novel zero shot quantization framework, 2020
Yaohui Cai, Zhewei Yao, Zhen Dong, Amir Gholami, Michael W. Mahoney, and Kurt Keutzer · 2020
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