arXiv:2606. 03292v1 Announce Type: cross Abstract: Many systems used in real-world environments require adding new categories and incorporating new information without forgetting what was previously learnt by the classification model.
The paper introduces the Continuous Evolution Pool (CEP), a replay‑free framework for online time series forecasting that tackles recurring concept drift. CEP maintains a dynamic pool of specialized forecasters, using lightweight statistical genes to identify concepts, spawn new models when distribution shifts occur, and prune obsolete ones under memory limits. Experiments on real‑world datasets show CEP reduces forecasting error by up to 24% compared to state‑of‑the‑art baselines, especially in scenarios with pronounced recurring drift.
arXiv:2603. 10046v2 Announce Type: replace Abstract: Wearable sensors in Internet of Things (IoT) ecosystems increasingly support applications such as remote health monitoring, elderly care, and smart home automation, all of which rely on robust human activity recognition (HAR).
arXiv:2609.06006v1 Announce Type: cross Abstract: Deep learning for time series has progressed through successive architectural paradigms, from recurrent networks and transformers to structured state...
The paper introduces a domain‑specific parameter‑isolation architecture for domain‑incremental learning (DIL) in audio classification, aiming to preserve knowledge from earlier domains without accessing their data. By employing data‑free generative replay and cross‑domain feature generation, the method constructs new experts conditioned on all previously frozen models, thereby mitigating catastrophic forgetting. Applied to the DCASE 2026 Challenge Task 7, the approach achieves micro and macro accuracies of 78.4 % and 78.9 %, outperforming the baseline by 33 and 25 percentage points, respectively, with ablation studies confirming the contribution of each component.
arXiv:2606. 29465v1 Announce Type: new Abstract: Class-incremental learning requires a model to learn new classes while preserving decision regions for old ones.
The paper presents a generative continual learning framework that extends self‑organizing maps (SOMs) with learned distributional statistics and encoder–decoder models for class‑incremental learning. By storing running means, variances, and covariances for each SOM unit, the method can generate synthetic samples for replay without storing raw data, enabling exemplar‑free learning. Experiments on CIFAR‑10, CIFAR‑100, and TinyImageNet show competitive or superior performance compared to state‑of‑the‑art memory‑based and memory‑free methods, and the approach also allows easy visualization and post‑training generative use.
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.
arXiv:2603. 11201v3 Announce Type: replace-cross Abstract: The world is inherently dynamic, and continual learning aims to enable models to adapt to ever-evolving data streams.
arXiv:2610.06942v1 Announce Type: new Abstract: Deep learning models, particularly recurrent neural networks and their variants, such as long short-term memory, have significantly advanced time serie...
arXiv:2602. 11550v2 Announce Type: replace-cross Abstract: Time Series Foundation Models (TSFMs) achieve strong zero-shot forecasting through large-scale pre-training, but adapting them to downstream domains under distribution shift remains challenging.
arXiv:2404.07729v2 Announce Type: replace Abstract: Continual learning (CL) evaluates adaptability in learning solutions to retain knowledge. Our research addresses the challenge of catastrophic forg...