Search Results for author:
Found 8 papers, 2 papers with code
AutoDFP: Automatic Data-Free Pruning via Channel Similarity Reconstruction
AutoDFP assesses the similarity of channels for each layer and provides this information to the reinforcement learning agent, guiding the pruning and reconstruction process of the network.
CR-SFP: Learning Consistent Representation for Soft Filter Pruning
In this paper, we propose to mitigate this gap by learning consistent representation for soft filter pruning, dubbed as CR-SFP.
MaxQ: Multi-Axis Query for N:M Sparsity Network
Meanwhile, a sparsity strategy that gradually increases the percentage of N:M weight blocks is applied, which allows the network to heal from the pruning-induced damage progressively.
SUBP: Soft Uniform Block Pruning for 1xN Sparse CNNs Multithreading Acceleration
To overcome them, this paper proposes a novel \emph{\textbf{S}oft \textbf{U}niform \textbf{B}lock \textbf{P}runing} (SUBP) approach to train a uniform 1$\times$N sparse structured network from scratch.
RGP: Neural Network Pruning through Its Regular Graph Structure
In this paper, we study the graph structure of the neural network, and propose regular graph based pruning (RGP) to perform a one-shot neural network pruning.
GGT: Graph-Guided Testing for Adversarial Sample Detection of Deep Neural Network
Deep Neural Networks (DNN) are known to be vulnerable to adversarial samples, the detection of which is crucial for the wide application of these DNN models.
Adversarial Sample Detection via Channel Pruning
Experiments on CIFAR10 and SVHN show that the FLOPs and size of our generated model are only 24. 46\% and 4. 86\% of the original model.
SigNet: A Novel Deep Learning Framework for Radio Signal Classification
More interestingly, our proposed models behave extremely well in small-sample learning when only a small training dataset is provided.
