24 citations · 25 across the 2 of their papers we have counts for
4 papers · 1 filter
CondenseNet V2: Sparse Feature Reactivation for Deep Networks
Le Yang, Haojun Jiang, Ruojin Cai +4
Reusing features in deep networks through dense connectivity is an effective way to achieve high computational efficiency. The recent proposed CondenseNet has shown that this mecha…
Revisiting Locally Supervised Learning: an Alternative to End-to-end Training
Yulin Wang, Zanlin Ni, Shiji Song +2
Due to the need to store the intermediate activations for back-propagation, end-to-end (E2E) training of deep networks usually suffers from high GPUs memory footprint. This paper a…
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification
Yulin Wang, Kangchen Lv, Rui Huang +3
The accuracy of deep convolutional neural networks (CNNs) generally improves when fueled with high resolution images. However, this often comes at a high computational cost and hig…
Resolution Adaptive Networks for Efficient Inference
Le Yang, Yizeng Han, Xi Chen +3
Adaptive inference is an effective mechanism to achieve a dynamic tradeoff between accuracy and computational cost in deep networks. Existing works mainly exploit architecture redu…