54 citations · 272 across the 29 of their papers we have counts for
7 papers · 1 filter
One for More: Selecting Generalizable Samples for Generalizable ReID Model
Enwei Zhang, Xinyang Jiang, Hao Cheng +7
Current training objectives of existing person Re-IDentification (ReID) models only ensure that the loss of the model decreases on selected training batch, with no regards to the 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…
Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Learning
Jinpeng Wang, Yuting Gao, Ke Li +7
Self-supervised learning has shown great potentials in improving the video representation ability of deep neural networks by getting supervision from the data itself. However, some…
Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion
Jinpeng Wang, Yuting Gao, Ke Li +5
One significant factor we expect the video representation learning to capture, especially in contrast with the image representation learning, is the object motion. However, we foun…
Filter Grafting for Deep Neural Networks: Reason, Method, and Cultivation
Hao Cheng, Fanxu Meng, Ke Li +4
Filter is the key component in modern convolutional neural networks (CNNs). However, since CNNs are usually over-parameterized, a pre-trained network always contain some invalid (u…
Architecture Disentanglement for Deep Neural Networks
Jie Hu, Liujuan Cao, Qixiang Ye +6
Understanding the inner workings of deep neural networks (DNNs) is essential to provide trustworthy artificial intelligence techniques for practical applications. Existing studies…