Learning to Weight Samples for Dynamic Early-exiting Networks

1 code implementation • 17 Sep 2022 • Yizeng Han, Yifan Pu, Zihang Lai, Chaofei Wang, Shiji Song, Junfen Cao, Wenhui Huang, Chao Deng, Gao Huang

Intuitively, easy samples, which generally exit early in the network during inference, should contribute more to training early classifiers.

Meta-Learning

CAM-loss: Towards Learning Spatially Discriminative Feature Representations

no code implementations • ICCV 2021 • Chaofei Wang, Jiayu Xiao, Yizeng Han, Qisen Yang, Shiji Song, Gao Huang

The backbone of traditional CNN classifier is generally considered as a feature extractor, followed by a linear layer which performs the classification.

Few-Shot Learning Knowledge Distillation +1

Adaptive Focus for Efficient Video Recognition

1 code implementation • ICCV 2021 • Yulin Wang, Zhaoxi Chen, Haojun Jiang, Shiji Song, Yizeng Han, Gao Huang

In this paper, we explore the spatial redundancy in video recognition with the aim to improve the computational efficiency.

Video Recognition

Dynamic Neural Networks: A Survey

no code implementations • 9 Feb 2021 • Yizeng Han, Gao Huang, Shiji Song, Le Yang, Honghui Wang, Yulin Wang

Dynamic neural network is an emerging research topic in deep learning.

Decision Making

Resolution Adaptive Networks for Efficient Inference

2 code implementations • CVPR 2020 • Le Yang, Yizeng Han, Xi Chen, Shiji Song, Jifeng Dai, Gao Huang

Adaptive inference is an effective mechanism to achieve a dynamic tradeoff between accuracy and computational cost in deep networks.