暂无分享,去创建一个
[1] Jian Sun,et al. Accelerating Very Deep Convolutional Networks for Classification and Detection , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[2] Sinno Jialin Pan,et al. Deep Neural Network Quantization via Layer-Wise Optimization Using Limited Training Data , 2019, AAAI.
[3] Zenglin Xu,et al. Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[4] Thad Starner,et al. Data-Free Knowledge Distillation for Deep Neural Networks , 2017, ArXiv.
[5] Kartikeya Bhardwaj,et al. Dream Distillation: A Data-Independent Model Compression Framework , 2019, ArXiv.
[6] Hongyi Zhang,et al. mixup: Beyond Empirical Risk Minimization , 2017, ICLR.
[7] Suyog Gupta,et al. To prune, or not to prune: exploring the efficacy of pruning for model compression , 2017, ICLR.
[8] Xiangyu Zhang,et al. Channel Pruning for Accelerating Very Deep Neural Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[9] Jianxin Wu,et al. ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[10] Hanan Samet,et al. Pruning Filters for Efficient ConvNets , 2016, ICLR.
[11] Geoffrey E. Hinton,et al. Distilling the Knowledge in a Neural Network , 2015, ArXiv.
[12] Wei Liu,et al. PocketFlow: An Automated Framework for Compressing and Accelerating Deep Neural Networks , 2018 .
[13] Elad Hoffer,et al. Scalable Methods for 8-bit Training of Neural Networks , 2018, NeurIPS.
[14] Jian Cheng,et al. Quantized Convolutional Neural Networks for Mobile Devices , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Jitendra Malik,et al. Cross Modal Distillation for Supervision Transfer , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Mingjie Sun,et al. Rethinking the Value of Network Pruning , 2018, ICLR.
[17] Sinno Jialin Pan,et al. Cooperative Pruning in Cross-Domain Deep Neural Network Compression , 2019, IJCAI.
[18] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[19] R. Venkatesh Babu,et al. Zero-Shot Knowledge Distillation in Deep Networks , 2019, ICML.
[20] Wei Wang,et al. RTN: Reparameterized Ternary Network , 2019, AAAI.
[21] Xin Dong,et al. Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon , 2017, NIPS.
[22] Zenglin Xu,et al. Structured Pruning of Recurrent Neural Networks through Neuron Selection , 2020, Neural Networks.
[23] Yoshua Bengio,et al. Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation , 2013, ArXiv.
[24] Yoshua Bengio,et al. FitNets: Hints for Thin Deep Nets , 2014, ICLR.
[25] Song Han,et al. Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.
[26] Chao Xu,et al. Data-Free Learning of Student Networks , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[27] Changshui Zhang,et al. Few Sample Knowledge Distillation for Efficient Network Compression , 2018, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Changshui Zhang,et al. Knowledge Distillation from Few Samples , 2018, ArXiv.
[29] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Yu Bai,et al. ProxQuant: Quantized Neural Networks via Proximal Operators , 2018, ICLR.
[31] Paul Tseng,et al. Exact Regularization of Convex Programs , 2007, SIAM J. Optim..
[32] Stephen P. Boyd,et al. Proximal Algorithms , 2013, Found. Trends Optim..