4 citations · 7 across the 6 of their papers we have counts for
6 papers · 1 filter
Semi-Supervised Regression with Heteroscedastic Pseudo-Labels
Xueqing Sun, Renzhen Wang, Quanziang Wang +3
Pseudo-labeling is a commonly used paradigm in semi-supervised learning, yet its application to semi-supervised regression (SSR) remains relatively under-explored. Unlike classific…
Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning
Wenyang Liao, Quanziang Wang, Yichen Wu +2
Replay-based continual learning (CL) methods assume that models trained on a small subset can also effectively minimize the empirical risk of the complete dataset. These methods ma…
Dual-CBA: Improving Online Continual Learning via Dual Continual Bias Adaptors from a Bi-level Optimization Perspective
Quanziang Wang, Renzhen Wang, Yichen Wu +3
In online continual learning (CL), models trained on changing distributions easily forget previously learned knowledge and bias toward newly received tasks. To address this issue,…
CBA: Improving Online Continual Learning via Continual Bias Adaptor
Quanziang Wang, Renzhen Wang, Yichen Wu +2
Online continual learning (CL) aims to learn new knowledge and consolidate previously learned knowledge from non-stationary data streams. Due to the time-varying training setting,…
Imbalanced Semi-supervised Learning with Bias Adaptive Classifier
Renzhen Wang, Xixi Jia, Quanziang Wang +2
Pseudo-labeling has proven to be a promising semi-supervised learning (SSL) paradigm. Existing pseudo-labeling methods commonly assume that the class distributions of training data…
Learning to generate imaginary tasks for improving generalization in meta-learning
Yichen Wu, Long-Kai Huang, Ying Wei
The success of meta-learning on existing benchmarks is predicated on the assumption that the distribution of meta-training tasks covers meta-testing tasks. Frequent violation of th…