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CASIA OpenIR
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Deep Neural Network Self-Distillation Exploiting Data Representation Invariance 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 卷号: 33, 期号: 1, 页码: 257-269
作者:  Xu, Ting-Bing;  Liu, Cheng-Lin
收藏  |  浏览/下载：191/0  |  提交时间：2022/02/16
Training  Nonlinear distortion  Data models  Neural networks  Knowledge engineering  Network architecture  Generalization error  network compression  representation invariance  self-distillation (SD)  
Dynamical Channel Pruning by Conditional Accuracy Change for Deep Neural Networks 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 卷号: 无, 期号: 无, 页码: 无
作者:  Chen, Zhiqiang;  Xu, Ting-Bing;  Du, Changde;  Liu, Cheng-Lin;  He, Huiguang
浏览  |  Adobe PDF(4352Kb)  |  收藏  |  浏览/下载：283/64  |  提交时间：2021/01/27
Conditional accuracy change (CAC), direct criterion, dynamical channel pruning, neural network compression, structure shaping.  
Discriminative Feature Selection via Employing Smooth and Robust Hinge Loss 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 卷号: 30, 期号: 3, 页码: 788-802
作者:  Peng, Hanyang;  Liu, Cheng-Lin
收藏  |  浏览/下载：226/0  |  提交时间：2019/07/12
Accelerated proximal gradient (APG)  extended hinge loss (HL)  feature selection  sparsity regularization