4 papers · 1 filter
Non-Forgetting Knowledge Allocation with Bi-level Competition for Class-Incremental Learning
Xiang Tan, Run He, Yawen Cui +6
Class-Incremental Learning (CIL) with pre-trained models (PTMs) aims to sequentially adapt PTMs to new categories without forgetting old knowledge. Built upon PTMs, existing adapte…
L3A: Label-Augmented Analytic Adaptation for Multi-Label Class Incremental Learning
Xiang Zhang, Run He, Jiao Chen +5
Class-incremental learning (CIL) enables models to learn new classes continually without forgetting previously acquired knowledge. Multi-label CIL (MLCIL) extends CIL to a real-wor…
Semantic Shift Estimation via Dual-Projection and Classifier Reconstruction for Exemplar-Free Class-Incremental Learning
Run He, Di Fang, Yicheng Xu +5
Exemplar-Free Class-Incremental Learning (EFCIL) aims to sequentially learn from distinct categories without retaining exemplars but easily suffers from catastrophic forgetting of…
F-OAL: Forward-only Online Analytic Learning with Fast Training and Low Memory Footprint in Class Incremental Learning
Huiping Zhuang, Yuchen Liu, Run He +5
Online Class Incremental Learning (OCIL) aims to train models incrementally, where data arrive in mini-batches, and previous data are not accessible. A major challenge in OCIL is C…