Aug 1, 2019 · We consider pruning these very small gradients randomly to accelerate CNN training according to the statistical distribution of activation gradients.

Experimental results show that our training approach could substantially achieve up to 5.92× speedups at back-propagation stage with negligible accuracy loss.

A general network expansion method that utilizes both width- and depth-level sparsity of dense models to accelerate the training of deep neural networks and ...

Hence, we consider pruning these very small gradients randomly to accelerate CNN training according to the statistical distribution of activation gradients.

Accelerating CNN Training by Pruning Activation Gradients [ECCV]; Adversarial ... Learning Filter Pruning Criteria for Deep Convolutional Neural Networks ...

Jun 9, 2024 · As far as we know, no previous work suggested using N:M fine-grained sparsity to accelerate the update phase, by pruning the neural gradients. 3 ...

Co-authors ; Accelerating CNN training by pruning activation gradients. X Ye, P Dai, J Luo, X Guo, Y Qi, J Yang, Y Chen. ECCV'20, 2020. 43, 2020 ; SparseTrain: ...

In this article, we'll discuss pruning neural networks: what it is, how it works, different pruning methods, and how to evaluate them.

Conversely, structured pruning [30, 40] involves the removal of entire channels or filters from the network, which can pose chal- lenges during model training ...

By means of pruning [34]- [39], we can decrease the amount of computation and memory needed for training, and concentrate resources on the weights that are ...