5 papers
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…
Rethinking Adapter Placement: A Dominant Adaptation Module Perspective
Suoxin Zhang, Run He, Di Fang +3
Low-rank adaptation (LoRA) is a widely used parameter-efficient fine-tuning method that places trainable low-rank adapters into frozen pre-trained models. Recent studies show that…
Analytic Continual Test-Time Adaptation for Multi-Modality Corruption
Yufei Zhang, Yicheng Xu, Hongxin Wei +4
Test-Time Adaptation (TTA) enables pre-trained models to bridge the gap between source and target datasets using unlabeled test data, addressing domain shifts caused by corruptions…
C-Adapter: Adapting Deep Classifiers for Efficient Conformal Prediction Sets
Kangdao Liu, Hao Zeng, Jianguo Huang +3
Conformal prediction, as an emerging uncertainty quantification technique, typically functions as post-hoc processing for the outputs of trained classifiers. To optimize the classi…
Advancing Cross-domain Discriminability in Continual Learning of Vision-Language Models
Yicheng Xu, Yuxin Chen, Jiahao Nie +3
Continual learning (CL) with Vision-Language Models (VLMs) has overcome the constraints of traditional CL, which only focuses on previously encountered classes. During the CL of VL…