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Mobile edge devices see increased demands in deep neural networks (DNNs) inference while suffering from stringent constraints in computing resources.
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Yann LeCun · 1998
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Greedy layer-wise training of deep networks
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Imagenet: A large-scale hierarchical image database
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Transforming auto-encoders
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Rethinking pre-training and self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin D Cubuk, and Quoc V Le · 2011
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Provable bounds for learning some deep representations
Sanjeev Arora, Aditya Bhaskara, Rong Ge, and Tengyu Ma · 2014
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On the complexity of neural network classifiers: A comparison between shallow and deep architectures
Monica Bianchini and Franco Scarselli · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Why are deep nets reversible: A simple theory, with implications for training
Sanjeev Arora, Yingyu Liang, and Tengyu Ma · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan · 2015
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Inceptionism: Going deeper into neural networks
Alexander Mordvintsev, Christopher Olah, and Mike Tyka · 2015
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Combining differential privacy and secure multiparty computation
Martin Pettai and Peeter Laud · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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The power of depth for feedforward neural networks
Ronen Eldan and Ohad Shamir · 2016
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Branchynet: Fast inference via early exiting from deep neural networks
Surat Teerapittayanon, Bradley McDanel, and Hsiang-Tsung Kung · 2016
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Benefits of depth in neural networks
Matus Telgarsky · 2016
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Learning to prune deep neural networks via layer-wise optimal brain surgeon
Xin Dong, Shangyu Chen, and Sinno Pan · 2017
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Towards understanding the invertibility of convolutional neural networks
Anna C. Gilbert, Yi Zhang, Kibok Lee, Yuting Zhang, and Honglak Lee · 2017
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Countering adversarial images using input transformations
Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens Van Der Maaten · 2017
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Neurosurgeon
Yiping Kang, Johann Hauswald, Cao Gao, Austin Rovinski, Trevor Mudge, Jason Mars, and Lingjia Tang · 2017
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On convergence and stability of gans
Naveen Kodali, Jacob Abernethy, James Hays, and Zsolt Kira · 2017
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Mmd gan: Towards deeper understanding of moment matching network
Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, and Barnabás Póczos · 2017
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Plug & play generative networks: Conditional iterative generation of images in latent space
Anh Nguyen, Jeff Clune, Yoshua Bengio, Alexey Dosovitskiy, and Jason Yosinski · 2017
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The knowledge within: Methods for data-free model compression
Matan Haroush, Itay Hubara, Elad Hoffer, and Daniel Soudry · 2020
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What shapes feature representations? exploring datasets, architectures, and training
Katherine Hermann and Andrew Lampinen · 2020
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Analyzing and improving the image quality of stylegan
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
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Head network distillation: Splitting distilled deep neural networks for resource-constrained edge computing systems
Yoshitomo Matsubara, Davide Callegaro, Sabur Baidya, Marco Levorato, and Sameer Singh · 2020
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A survey on distributed machine learning
Joost Verbraeken, Matthijs Wolting, Jonathan Katzy, Jeroen Kloppenburg, Tim Verbelen, and Jan S Rellermeyer · 2020
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Generative low-bitwidth data free quantization
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On the expressive power of deep neural networks
Maithra Raghu, Ben Poole, Jon Kleinberg, Surya Ganguli, and Jascha Sohl-Dickstein · 2017
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Distributed deep neural networks over the cloud, the edge and end devices
Surat Teerapittayanon, Bradley McDanel, and Hsiang-Tsung Kung · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Demystifying mmd gans
Mikołaj Bińkowski, Dougal J Sutherland, Michael Arbel, and Arthur Gretton · 2018
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Rethinking imagenet pre-training
Kaiming He Ross Girshick Piotr Dollár · 2018
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i-revnet: Deep invertible networks
Jörn-Henrik Jacobsen, Arnold Smeulders, and Edouard Oyallon · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Diederik P Kingma and Prafulla Dhariwal · 2018
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Shoukai Xu, Haokun Li, Bohan Zhuang, Jing Liu, Jiezhang Cao, Chuangrun Liang, and Mingkui Tan · 2020
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Deep compressive offloading: Speeding up neural network inference by trading edge computation for network latency
Shuochao Yao, Jinyang Li, Dongxin Liu, Tianshi Wang, Shengzhong Liu, Huajie Shao, and Tarek Abdelzaher · 2020
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Dreaming to distill: Data-free knowledge transfer via deepinversion
Hongxu Yin, Pavlo Molchanov, Jose M. Alvarez, Zhizhong Li, Arun Mallya, Derek Hoiem, Niraj K Jha, and Jan Kautz · 2020
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The secret revealer: Generative model-inversion attacks against deep neural networks
Yuheng Zhang, Ruoxi Jia, Hengzhi Pei, Wenxiao Wang, Bo Li, and Dawn Song · 2020
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Differentiable augmentation for data-efficient gan training
Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han · 2020
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R-gap: Recursive gradient attack on privacy
Junyi Zhu and Matthew Blaschko · 2020
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Private-knn: Practical differential privacy for computer vision
Yuqing Zhu, Xiang Yu, Manmohan Chandraker, and Yu-Xiang Wang · 2020
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https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio , 2021
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Generative adversarial network: An overview of theory and applications
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Understanding and mitigating exploding inverses in invertible neural networks
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Data-free knowledge distillation for object detection
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Github - kwotsin/mimicry: [cvpr 2020 workshop] a pytorch gan library that reproduces research results for popular gans., Jun 2021
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Split computing and early exiting for deep learning applications: Survey and research challenges
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Understanding invariance via feedforward inversion of discriminatively trained classifiers
Piotr Teterwak, Chiyuan Zhang, Dilip Krishnan, and Michael C Mozer · 2021
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Practical defences against model inversion attacks for split neural networks
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On data augmentation for gan training
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Variational model inversion attacks
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See through gradients: Image batch recovery via gradinversion
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Splitnets: Designing neural architectures for efficient distributed computing on head-mounted systems
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Feature space hijacking attacks against differentially private split learning
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Auditing privacy defenses in federated learning via generative gradient leakage
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