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:2608.20710v1 Announce Type: new
Abstract: Real-world semi-supervised learning (SSL) often encounters significant challenges with long-tailed label distributions and noisy pseudo-labels, which h...
By Hongyang He, Xinyuan Song, Yan Zhong, Daizong Liu, Yanbin Li, Yang-fan He, Wenqiao Zhang
arXiv:2606. 25347v1 Announce Type: new Abstract: Exemplar-free class-incremental learning (EFCIL) requires stable decision boundaries within a shifting feature space.
By Hongye Xu, Bartosz Krawczyk
arXiv:2606. 27095v1 Announce Type: cross Abstract: Cold-start exemplar-free class-incremental learning requires learning a growing set of classes without replay, external pretraining, or a large initial task.
By Augustinas Ju\v{c}as, Yangchen Pan
arXiv:2604. 02765v2 Announce Type: replace Abstract: Class-incremental learning (CIL) is commonly evaluated under predefined schedules with fixed or nearly equal class increments, leaving irregular class-arrival scenarios underexplored.
By Zhiming Xu, Baile Xu, Jian Zhao, Furao Shen, Suorong Yang
Geo-LoRA introduces a geometry‑aware framework for low‑rank adaptation in rehearsal‑free class‑incremental learning. It regulates the evolution of shared and task‑specific LoRA subspaces using Subspace Projection Preservation, Adaptive Core‑Slack Alignment, and Median‑Calibrated Block Overlap, ensuring smooth trajectories on the Grassmann manifold and balanced stability‑plasticity trade‑offs. The method achieves state‑of‑the‑art performance across multiple benchmarks without adding new adapter types.
By Yibo Feng