4 citations · 7 across the 9 of their papers we have counts for
13 papers
Exact Endpoint Asymptotics and Almost-Sure Limits for Uniform Empirical Measures in Two Dimensions
Weitao Huang, Yichen Wu, Renzhen Wang +1
For the empirical measure of the first points of one infinite sequence of independent uniform samples in the unit square, we identify the leading constant in the bounded-Lipsch…
MAC-Splat: Multi-Attribute Consistency for High-Fidelity Sparse-View Reconstruction
Jinqian Yang, Yichen Wu, Wanhua Li +4
Reconstructing high-fidelity 3D scenes from sparse-views remains a central problem in generalizable neural rendering. Existing generalizable 3D Gaussian Splatting (3DGS) methods of…
IllumFlow: Illumination-Adaptive Low-Light Enhancement via Conditional Rectified Flow and Retinex Decomposition
Wenyang Wei, Yang yang, Xixi Jia +3
We present IllumFlow, a novel framework that synergizes conditional Rectified Flow (CRF) with Retinex theory for low-light image enhancement (LLIE). Our model addresses low-light e…
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…
SD-LoRA: Scalable Decoupled Low-Rank Adaptation for Class Incremental Learning
Yichen Wu, Hongming Piao, Long-Kai Huang +6
Continual Learning (CL) with foundation models has recently emerged as a promising paradigm to exploit abundant knowledge acquired during pre-training for tackling sequential tasks…
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…
Relational Experience Replay: Continual Learning by Adaptively Tuning Task-wise Relationship
Quanziang Wang, Renzhen Wang, Yuexiang Li +4
Continual learning is a promising machine learning paradigm to learn new tasks while retaining previously learned knowledge over streaming training data. Till now, rehearsal-based…
Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep Classifiers
Renzhen Wang, De cai, Kaiwen Xiao +3
Hierarchical classification aims to sort the object into a hierarchical structure of categories. For example, a bird can be categorized according to a three-level hierarchy of orde…
Unsupervised Local Discrimination for Medical Images
Huai Chen, Renzhen Wang, Xiuying Wang +7
Contrastive learning, which aims to capture general representation from unlabeled images to initialize the medical analysis models, has been proven effective in alleviating the hig…
Residual Moment Loss for Medical Image Segmentation
Quanziang Wang, Renzhen Wang, Yuexiang Li +3
Location information is proven to benefit the deep learning models on capturing the manifold structure of target objects, and accordingly boosts the accuracy of medical image segme…