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Status: Unchecked ×Keyword: stochastic gradient estimation ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Joint Multi-Dimension Pruning via Numerical Gradient Update

   Liu, Zhang, Shen, Wei, Cheng and Sun · IEEE Transactions on Image Processing · 2021

   The paper presents JointPruning, a method that prunes a neural network across spatial size, depth and channels at once using estimated gradients, and tests it on ImageNet with MobileNet and ResNet.

   Unchecked2 claims
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   1. UncheckedPruning a network's spatial size, depth and channels together in one training run finds better, more efficient results than tuning one dimension alone.“We show that the joint strategy discovers a better status than previous studies that focused on individual dimensions solely, as our method is optimized collaboratively across the three dimensions in a single end-to-end training and it is more efficient than the previous exhaustive methods.”
   2. UncheckedJointPruning improves on the state-of-the-art by 2.5% and 2.6% on the compact MobileNet V1 and V2 under a very large compression ratio.“For instance, we achieve significant margins of 2.5% and 2.6% improvement over the state-of-the-art approach on the already compact MobileNet V1&V2 under an extremely large compression ratio.”