arXiv:2606. 31275v1 Announce Type: cross Abstract: Online Continual Self-Supervised Learning (OCSSL) aims to learn representations from a continuous stream of unlabeled data, without knowledge of task boundaries and under memory constraints.
arXiv:2607. 09785v1 Announce Type: cross Abstract: Traditionally, continual learning has assumed access to labeled data, yet many real-world applications -- such as lifelong robotics -- require models to adapt continuously from unlabeled streams.
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.
The paper introduces Multiple Embedding Replay Selection (MERS), a graph‑based method that combines supervised and self‑supervised embeddings to improve sample selection for replay buffers in continual learning. MERS replaces traditional buffer selection modules and demonstrates consistent performance gains over state‑of‑the‑art strategies, especially in low‑memory settings. Experiments on CIFAR‑100 and TinyImageNet show that MERS outperforms single‑embedding baselines without adding model parameters or increasing replay volume, making it a practical, drop‑in enhancement for replay‑based continual learning.
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.
arXiv:2609.18444v1 Announce Type: new Abstract: Weakly supervised class-incremental semantic segmentation (WILSS) aims to train a segmentation model over multiple steps, each introducing new concepts...
The paper argues that catastrophic forgetting and loss of plasticity alone cannot explain why naive sequential training underperforms offline joint training. It introduces data co-observation as a third factor, showing that observing training data together consistently improves performance across supervised and self-supervised settings. The study also reinterprets common continual learning methods, suggesting that memory replay’s success stems from restoring co-observation benefits rather than merely mitigating forgetting.
The paper introduces sLoTh, a parameter‑efficient continual learning framework for sparse event‑based vision transformers. sLoTh freezes the backbone and limits plasticity to low‑rank attention updates (seLoRA) and shared neuronal threshold modulation, updating less than 1% of parameters without replay buffers. Experiments on CIFAR‑100, Tiny‑ImageNet, ImageNet‑100, and ImageNet‑R show competitive rehearsal‑free performance across up to 100 tasks while achieving roughly 6.5× lower energy consumption than dense vision transformers.
The paper introduces sLoTh, a parameter‑efficient continual learning framework for sparse event‑based vision transformers. By freezing the backbone and limiting plasticity to low‑rank attention updates (seLoRA) and shared neuronal threshold modulation, sLoTh adapts to new tasks while updating less than 1% of the parameters and avoiding replay buffers. Experiments on CIFAR‑100, Tiny‑ImageNet, ImageNet‑100, and ImageNet‑R show competitive rehearsal‑free performance across up to 100 tasks and achieve roughly 6.5× lower energy consumption than dense vision transformers.
arXiv:2605. 20247v2 Announce Type: replace-cross Abstract: Catastrophic forgetting remains a major obstacle to continual learning in large language models (LLMs) and vision--language models (VLMs).
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:2509. 13211v4 Announce Type: replace Abstract: The ability to learn continuously over time remains a major challenge for modern machine learning systems, even in the era of Foundation Models.
arXiv:2606. 07474v1 Announce Type: new Abstract: Unsupervised Continual Learning (UCL) aims to enable neural networks to learn sequential tasks without labels or access to past data.