30 citations · 54 across the 12 of their papers we have counts for
13 papers
FedSWA: Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging
Liu junkang, Yuanyuan Liu, Fanhua Shang +3
For federated learning (FL) algorithms such as FedSAM, their generalization capability is crucial for real-word applications. In this paper, we revisit the generalization problem i…
NUC-Net: Non-uniform Cylindrical Partition Network for Efficient LiDAR Semantic Segmentation
Xuzhi Wang, Wei Feng, Lingdong Kong +1
LiDAR semantic segmentation plays a vital role in autonomous driving. Existing voxel-based methods for LiDAR semantic segmentation apply uniform partition to the 3D LiDAR point clo…
Beyond Background Shift: Rethinking Instance Replay in Continual Semantic Segmentation
Hongmei Yin, Tingliang Feng, Fan Lyu +4
In this work, we focus on continual semantic segmentation (CSS), where segmentation networks are required to continuously learn new classes without erasing knowledge of previously…
Casual Inference via Style Bias Deconfounding for Domain Generalization
Jiaxi Li, Di Lin, Hao Chen +3
Deep neural networks (DNNs) often struggle with out-of-distribution data, limiting their reliability in diverse realworld applications. To address this issue, domain generalization…
Towards stable training of parallel continual learning
Li Yuepan, Fan Lyu, Yuyang Li +3
Parallel Continual Learning (PCL) tasks investigate the training methods for continual learning with multi-source input, where data from different tasks are learned as they arrive.…
Controllable Continual Test-Time Adaptation
Ziqi Shi, Fan Lyu, Ye Liu +5
Continual Test-Time Adaptation (CTTA) is an emerging and challenging task where a model trained in a source domain must adapt to continuously changing conditions during testing, wi…
Overcoming Domain Drift in Online Continual Learning
Fan Lyu, Daofeng Liu, Linglan Zhao +5
Online Continual Learning (OCL) empowers machine learning models to acquire new knowledge online across a sequence of tasks. However, OCL faces a significant challenge: catastrophi…
Elastic Multi-Gradient Descent for Parallel Continual Learning
Fan Lyu, Wei Feng, Yuepan Li +4
The goal of Continual Learning (CL) is to continuously learn from new data streams and accomplish the corresponding tasks. Previously studied CL assumes that data are given in sequ…
Long-Tailed Learning as Multi-Objective Optimization
Weiqi Li, Fan Lyu, Fanhua Shang +2
Real-world data is extremely imbalanced and presents a long-tailed distribution, resulting in models that are biased towards classes with sufficient samples and perform poorly on r…
Open Compound Domain Adaptation with Object Style Compensation for Semantic Segmentation
Tingliang Feng, Hao Shi, Xueyang Liu +4
Many methods of semantic image segmentation have borrowed the success of open compound domain adaptation. They minimize the style gap between the images of source and target domain…
Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer
Zhijin Ge, Fanhua Shang, Hongying Liu +4
Deep neural networks are vulnerable to adversarial examples crafted by applying human-imperceptible perturbations on clean inputs. Although many attack methods can achieve high suc…
Multi-Label Continual Learning using Augmented Graph Convolutional Network
Kaile Du, Fan Lyu, Linyan Li +5
Multi-Label Continual Learning (MLCL) builds a class-incremental framework in a sequential multi-label image recognition data stream. The critical challenges of MLCL are the constr…
Exploring Example Influence in Continual Learning
Qing Sun, Fan Lyu, Fanhua Shang +2
Continual Learning (CL) sequentially learns new tasks like human beings, with the goal to achieve better Stability (S, remembering past tasks) and Plasticity (P, adapting to new ta…