Channel Pruning in Quantization-aware Training: an Adaptive Projection-gradient Descent-shrinkage-splitting Method

Author:

Li Zhijian1,Xin Jack1

Affiliation:

1. University of California, Irvine,Department of Mathematics,Irvine,USA

Publisher

IEEE

Reference17 articles.

1. Learning structured sparsity in deep neural networks;wen;Advances in neural information processing systems,2016

2. Training neural networks without gradients: A scalable admm approach;taylor;International Conference on Machine Learning,2016

3. Adversarial Robustness vs. Model Compression, or Both?

4. Channel Pruning for Deep Neural Networks Via a Relaxed Groupwise Splitting Method

5. A channel-pruned and weightbinarized convolutional neural network for keyword spotting;lyu;Advanced Computational Methods for Knowledge Engineering ICCSAMA 2019,2020

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1. Training and approximation methods for realizing computationally-cheaper binarized neural networks;ETLTC-ICETM2023 INTERNATIONAL CONFERENCE PROCEEDINGS: ICT Integration in Technical Education & Entertainment Technologies and Management;2023

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