15 citations · 53 across the 9 of their papers we have counts for
4 papers · 1 filter
RMNet: Equivalently Removing Residual Connection from Networks
Fanxu Meng, Hao Cheng, Jiaxin Zhuang +2
Although residual connection enables training very deep neural networks, it is not friendly for online inference due to its multi-branch topology. This encourages many researchers…
An Empirical Study and Analysis on Open-Set Semi-Supervised Learning
Huixiang Luo, Hao Cheng, Fanxu Meng +4
Pseudo-labeling (PL) and Data Augmentation-based Consistency Training (DACT) are two approaches widely used in Semi-Supervised Learning (SSL) methods. These methods exhibit great p…
Pruning Filter in Filter
Fanxu Meng, Hao Cheng, Ke Li +4
Pruning has become a very powerful and effective technique to compress and accelerate modern neural networks. Existing pruning methods can be grouped into two categories: filter pr…
Filter Grafting for Deep Neural Networks
Fanxu Meng, Hao Cheng, Ke Li +4
This paper proposes a new learning paradigm called filter grafting, which aims to improve the representation capability of Deep Neural Networks (DNNs). The motivation is that DNNs…