Search Results for author:
Found 2 papers, 0 papers with code
Pruning at a Glance: Global Neural Pruning for Model Compression
We prove the viability of our method by producing highly compressed models, namely VGG-16, ResNet-56, and ResNet-110 respectively on CIFAR10 without losing any performance compared to the baseline, as well as ResNet-34 and ResNet-50 on ImageNet without a significant loss of accuracy.
Pruning at a Glance: A Structured Class-Blind Pruning Technique for Model Compression
High performance of deep learning models typically comes at cost of considerable model size and computation time.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
