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.
By Sergi Masip, Alicja Dobrzeniecka, Jonathan Swinnen, Joachim Collin, Bart{\l}omiej Twardowski, Szymon {\L}ukasik, Tinne Tuytelaars
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
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.
By Danit Yanowsky, Daphna Weinshall
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.
By Pujan Thapa, Alexander Ororbia, Travis Desell
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...
By Leon Arthur Marx, Francesco Barbato, Matteo Caligiuri, Pietro Zanuttigh
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.
By Timm Hess, Abhishek Jha, Gido M. van de Ven, Tinne Tuytelaars