The paper introduces AIR, an analytic imbalance rectifier designed for continual learning scenarios where data streams are evolving and imbalanced. AIR operates without exemplars, using a frozen backbone for feature extraction and a closed‑form incremental classifier that incorporates a class‑weighted ridge objective. It employs an analytic reweighting module to equalize sample weights across classes, achieving significant improvements in accuracy and macro F1 over 28 baseline methods in long‑tailed class‑incremental learning and over 15 baselines in the Si‑Blurry setting with recurring classes.
By Di Fang, Yinan Zhu, Zhiping Lin, Cen Chen, Ziqian Zeng, Huiping Zhuang
arXiv:2609.39839v1 Announce Type: new
Abstract: Class-Incremental Learning (CIL) aims to continuously learn new classes without forgetting previously acquired knowledge. Parameter-efficient fine-tuni...
By Hongwei Zhao (School of Computer Science,Engineering, Beihang University), Rui Liu (School of Computer Science,Engineering, Beihang University), Yansong Liu (School of Computer Science,Engineering, Beihang University)
The paper introduces TALON, a Task‑Adaptive LoRA‑Teacher framework for Few‑Shot Class‑Incremental Learning. TALON assigns a dedicated LoRA‑Teacher to each incremental task, then distills the frozen teachers into a single LoRA‑Student via Ensemble Knowledge Transfer, using a semantic‑guided weighting scheme to reduce forgetting and overfitting. Experiments on four FSCIL benchmarks show that TALON matches or surpasses state‑of‑the‑art accuracy while using up to 33× fewer deployment parameters and cutting inference time by 41.7%.
By Hongwei Zhao (School of Computer Science,Engineering, Beihang University), Rui Liu (School of Computer Science,Engineering, Beihang University), Yansong Liu (School of Computer Science,Engineering, Beihang University), Zhiyuan Zou (School of Computer Science,Engineering, Beihang University), Yong Chen (School of Computer Science, Beijing University of Posts,Telecommunications)
arXiv:2508.21424v3 Announce Type: replace
Abstract: Deep learning models have achieved state-of-the-art performance in many computer vision tasks. However, in real-world scenarios, novel classes that...
By Lucas Rakotoarivony
arXiv:2606. 05675v1 Announce Type: new Abstract: Continual learning (CL) seeks models that acquire new skills without erasing prior knowledge.
By Hongye Xu, Bartosz Krawczyk
arXiv:2608.31096v1 Announce Type: cross
Abstract: Class-incremental learning (CIL) requires a model to incrementally learn tasks that contain new classes without accessing earlier training data while...
By Yunxiang Fu, Meng Lou, Yizhou Yu