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We present any-precision deep neural networks (DNNs), which are trained with a new method that allows the learned DNNs to be flexible in numerical precision during inference.
Universally slimmable networks and improved training techniques
Yu, J.; and Huang, T. 2019 · 1903
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Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation
Chen, X.; Xie, L.; Wu, J.; and Tian, Q. 2019 · 1904
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HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision
Dong, Z.; Yao, Z.; Gholami, A.; Mahoney, M.; and Keutzer, K. 2019 · 1905
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Howard, A.; Sandler, M.; Chu, G.; Chen, L.-C.; Chen, B.; Tan, M.; Wang, W.; Zhu, Y.; Pang, R.; Vasudevan, V.; et al. 2019 · 1905
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EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Tan, M.; and Le, Q. V. 2019 · 1905
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Data-Free Quantization through Weight Equalization and Bias Correction
Nagel, M.; van Baalen, M.; Blankevoort, T.; and Welling, M. 2019 · 1906
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ImageNet: A Large-Scale Hierarchical Image Database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
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Semantic Contours from Inverse Detectors
Hariharan, B.; Arbelaez, P.; Bourdev, L.; Maji, S.; and Malik, J. 2011 · 2011
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Reading digits in natural images with unsupervised feature learning
Netzer, Y.; Wang, T.; Coates, A.; Bissacco, A.; Wu, B.; and Ng, A. Y. 2011 · 2011
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Neural Networks for Machine Learning
Hinton, G. 2012 · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012 · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Y.; Léonard, N.; and Courville, A. 2013 · 2013
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Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. 2014 · 2014
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The pascal visual object classes challenge: A retrospective
Everingham, M.; Eslami, S. A.; Van Gool, L.; Williams, C. K.; Winn, J.; and Zisserman, A. 2015 · 2015
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Explaining and Harnessing Adversarial Examples
Goodfellow, I.; Shlens, J.; and Szegedy, C. 2015 · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2015 · 2015
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Distilling the knowledge in a neural network
Hinton, G.; Vinyals, O.; and Dean, J. 2015 · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S.; and Szegedy, C. 2015 · 2015
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Proxylessnas: Direct neural architecture search on target task and hardware
Cai, H.; Zhu, L.; and Han, S. 2018 · 2018
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Pact: Parameterized clipping activation for quantized neural networks
Choi, J.; Wang, Z.; Venkataramani, S.; Chuang, P. I.-J.; Srinivasan, V.; and Gopalakrishnan, K. 2018 · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M.; Howard, A.; Zhu, M.; Zhmoginov, A.; and Chen, L.-C. 2018 · 2018
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Convolutional networks with adaptive inference graphs
Veit, A.; and Belongie, S. 2018 · 2018
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Blockdrop: Dynamic inference paths in residual networks
Wu, Z.; Nagarajan, T.; Kumar, A.; Rennie, S.; Davis, L. S.; Grauman, K.; and Feris, R. 2018 · 2018
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Courbariaux, M.; Hubara, I.; Soudry, D.; El-Yaniv, R.; and Bengio, Y. 2016 · 2016
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Rastegari, M.; Ordonez, V.; Redmon, J.; and Farhadi, A. 2016 · 2016
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Branchynet: Fast inference via early exiting from deep neural networks
Teerapittayanon, S.; McDanel, B.; and Kung, H.-T. 2016 · 2016
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Zhou, S.; Wu, Y.; Ni, Z.; Zhou, X.; Wen, H.; and Zou, Y. 2016 · 2016
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Deep learning with low precision by half-wave gaussian quantization
Cai, Z.; He, X.; Sun, J.; and Vasconcelos, N. 2017 · 2017
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Rethinking atrous convolution for semantic image segmentation
Chen, L.-C.; Papandreou, G.; Schroff, F.; and Adam, H. 2017 · 2017
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Spatially adaptive computation time for residual networks
Figurnov, M.; Collins, M. D.; Zhu, Y.; Zhang, L.; Huang, J.; Vetrov, D.; and Salakhutdinov, R. 2017 · 2017
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Yu, J.; Yang, L.; Xu, N.; Yang, J.; and Huang, T. 2018 · 2018
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Lq-nets: Learned quantization for highly accurate and compact deep neural networks
Zhang, D.; Yang, J.; Ye, D.; and Hua, G. 2018 · 2018
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A modulation module for multi-task learning with applications in image retrieval
Zhao, X.; Li, H.; Shen, X.; Liang, X.; and Wu, Y. 2018 · 2018
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Towards effective low-bitwidth convolutional neural networks
Zhuang, B.; Shen, C.; Tan, M.; Liu, L.; and Reid, I. 2018 · 2018
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Post training 4-bit quantization of convolutional networks for rapid-deployment
Banner, R.; Nahshan, Y.; and Soudry, D. 2019 · 2019
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Regularizing activation distribution for training binarized deep networks
Ding, R.; Chin, T.-W.; Liu, Z.; and Marculescu, D. 2019 · 2019
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Learning to quantize deep networks by optimizing quantization intervals with task loss
Jung, S.; Son, C.; Lee, S.; Son, J.; Han, J.-J.; Kwak, Y.; Hwang, S. J.; and Choi, C. 2019 · 2019
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HAQ: Hardware-Aware Automated Quantization with Mixed Precision
Wang, K.; Liu, Z.; Lin, Y.; Lin, J.; and Han, S. 2019 · 2019
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Mobinet: A mobile binary network for image classification
Phan, H.; He, Y.; Savvides, M.; Shen, Z.; et al. 2020 · 2020
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