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Found 5 papers, 4 papers with code
POCKET: Pruning Random Convolution Kernels for Time Series Classification from a Feature Selection Perspective
In Stage 1, POCKET utilizes dynamically varying penalties to efficiently achieve group sparsity within the classifier, removing features associated with zero weights and their corresponding kernels.
WHC: Weighted Hybrid Criterion for Filter Pruning on Convolutional Neural Networks
Filter pruning has attracted increasing attention in recent years for its capacity in compressing and accelerating convolutional neural networks.
Sub-network Multi-objective Evolutionary Algorithm for Filter Pruning
Filter pruning is a common method to achieve model compression and acceleration in deep neural networks (DNNs). Some research regarded filter pruning as a combinatorial optimization problem and thus used evolutionary algorithms (EA) to prune filters of DNNs.
Joint Matrix Decomposition for Deep Convolutional Neural Networks Compression
To overcome this problem, we propose to compress CNNs and alleviate performance degradation via joint matrix decomposition, which is different from existing works that compressed layers separately.
Efficient Neural Network
Matrix Factorization / Decomposition
+1
Deep convolutional neural network compression via coupled tensor decomposition
Comparing to the state-of-the-art independent matrix and tensor decomposition based methods, our model can obtain a better network performance under the same compression ratio.
