From the 1 of 16 linked papers with an AI index.
4 papers · 1 filter
REAL: Representation Enhanced Analytic Learning for Exemplar-free Class-incremental Learning
Run He, Di Fang, Yizhu Chen +5
Exemplar-free class-incremental learning (EFCIL) aims to mitigate catastrophic forgetting in class-incremental learning (CIL) without available historical training samples as exemp…
Online Analytic Exemplar-Free Continual Learning with Large Models for Imbalanced Autonomous Driving Task
Huiping Zhuang, Di Fang, Kai Tong +4
In autonomous driving, even a meticulously trained model can encounter failures when facing unfamiliar scenarios. One of these scenarios can be formulated as an online continual le…
GACL: Exemplar-Free Generalized Analytic Continual Learning
Huiping Zhuang, Yizhu Chen, Di Fang +5
Class incremental learning (CIL) trains a network on sequential tasks with separated categories in each task but suffers from catastrophic forgetting, where models quickly lose pre…
AIR: Analytic Imbalance Rectifier for Continual Learning
Di Fang, Yinan Zhu, Runze Fang +3
Continual learning enables AI models to learn new data sequentially without retraining in real-world scenarios. Most existing methods assume the training data are balanced, aiming…