The paper introduces HiDeR, a High Information Density Replay framework for Lifelong Person Re-Identification that replaces discrete sample selection with information compression. It uses a complexity‑aware memory allocation based on intra‑class variance and a metric‑guided condensation objective to preserve essential identity topologies, while a cross‑modality adaptation strategy bridges synthetic and real styles to improve training. Experiments show HiDeR outperforms state‑of‑the‑art methods in knowledge retention and generalization, and reduces cumulative replay cost.
By Mingyu Wang, Wei Jiang, Haojie Liu, Zhiyong Li, Weijie Mao
arXiv:2606. 02228v1 Announce Type: cross Abstract: Predicting whether an individual with Alzheimer's disease will experience mild or severe disease progression is essential for personalized treatment.
By Clara Hoffmann, Nadja Klein
arXiv:2608. 14634v1 Announce Type: new Abstract: Biological intelligence naturally prevents catastrophic forgetting through Complementary Learning Systems (CLS) theory, a macroscopic consolidation process driven at the local level by synaptic metaplasticity: the continuous, history-dependent neuromodulation of individual synapses.
By Isabelle Aguilar, Zayn Andre Zainal, Omid Kavehei
arXiv:2511. 04666v4 Announce Type: replace Abstract: A fundamental challenge in developing general learning algorithms is their tendency to forget past knowledge as they adapt to new data.
By Ben Sanati, Thomas L. Lee, Trevor McInroe, Aidan Scannell, Esmeralda S. Whitammer, David Abel, Amos Storkey
arXiv:2610.02126v1 Announce Type: cross
Abstract: We explore catastrophic forgetting in the context of large pre-trained models. By considering forgetting as a geometric problem in the input space of...
By Assaf Ben-Kish, Akarsh Kumar, James Glass, Raja Giryes
arXiv:2609.37587v1 Announce Type: cross
Abstract: Longitudinal electronic health record (EHR) modeling requires integrating new visits with an expanding patient history. Yet the continual accumulatio...
By Zijie Meng, Xiwei Dai, Yingying Zhang, Jian Wu, Xian Wu, Zuozhu Liu
arXiv:2507. 08922v3 Announce Type: replace-cross Abstract: Continual learning is an online paradigm where a learner continually accumulates knowledge from different tasks encountered over sequential time steps.
By Tameem Adel
arXiv:2504. 13822v3 Announce Type: replace-cross Abstract: The emergence of large pre-trained networks has revolutionized the AI field, unlocking new possibilities and achieving unprecedented performance.
By Eric Nuertey Coleman, Luigi Quarantiello, Ziyue Liu, Qinwen Yang, Samrat Mukherjee, Julio Hurtado, Vincenzo Lomonaco
The paper introduces a generative continual learning framework that uses growing self‑organizing maps (GSOMs) enhanced with learned distributional statistics and encoder‑decoder models for class‑incremental learning. GSOM units store mean, variance, and covariance estimates to synthesize replay samples, enabling exemplar‑free training without raw data or explicit task boundaries. Experiments on multiple benchmarks show that the unsupervised method competes with supervised memory‑based approaches and outperforms memory‑free baselines, especially in single‑class incremental scenarios, and provides baseline results for TinyImageNet and MiniImageNet.
By Pujan Thapa, Alexander Ororbia, Travis Desell
arXiv:2605.19750v2 Announce Type: replace
Abstract: Visual autoregressive (VAR) models have recently emerged as an efficient paradigm for text-to-image generation, yet their personalization capabilit...
By Junhao Li, Xinhao Zhong, Yi sun, Yuxia Qiao, Bin Chen, Shu-Tao Xia, Yaowei Wang
arXiv:2603. 11395v3 Announce Type: replace-cross Abstract: Continual reinforcement learning challenges agents to acquire new skills while retaining previously learned ones with the goal of improving performance in both past and future tasks.
By Abdulaziz Alyahya, Abdallah Al Siyabi, Markus R. Ernst, Luke Yang, Levin Kuhlmann, Gideon Kowadlo
arXiv:2605. 20297v2 Announce Type: replace-cross Abstract: Medical image segmentation faces a fundamental challenge in continual learning: data arrives sequentially from heterogeneous sources, yet effective continual learning requires discovering which tasks share sufficient structure to benefit from joint learning.
By Ziyuan Gao