15 citations · 24 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…