42 citations · 64 across the 6 of their papers we have counts for
6 papers
Effective Decision Boundary Learning for Class Incremental Learning
Kunchi Li, Jun Wan, Shan Yu
Rehearsal approaches in class incremental learning (CIL) suffer from decision boundary overfitting to new classes, which is mainly caused by two factors: insufficiency of old class…
Differentially Private Deep Learning with ModelMix
Hanshen Xiao, Jun Wan, Srinivas Devadas
Training large neural networks with meaningful/usable differential privacy security guarantees is a demanding challenge. In this paper, we tackle this problem by revisiting the two…
Effective Vision Transformer Training: A Data-Centric Perspective
Benjia Zhou, Pichao Wang, Jun Wan +2
Vision Transformers (ViTs) have shown promising performance compared with Convolutional Neural Networks (CNNs), but the training of ViTs is much harder than CNNs. In this paper, we…
Combining Self-Supervised and Supervised Learning with Noisy Labels
Yongqi Zhang, Hui Zhang, Quanming Yao +1
Since convolutional neural networks (CNNs) can easily overfit noisy labels, which are ubiquitous in visual classification tasks, it has been a great challenge to train CNNs against…
DeepGini: Prioritizing Massive Tests to Enhance the Robustness of Deep Neural Networks
Yang Feng, Qingkai Shi, Xinyu Gao +3
Deep neural networks (DNN) have been deployed in many software systems to assist in various classification tasks. In company with the fantastic effectiveness in classification, DNN…
Cooperative Training of Deep Aggregation Networks for RGB-D Action Recognition
Pichao Wang, Wanqing Li, Jun Wan +2
A novel deep neural network training paradigm that exploits the conjoint information in multiple heterogeneous sources is proposed. Specifically, in a RGB-D based action recognitio…