arXiv Machine Learning
Sep 17

Leveraging Complementary Embeddings for Replay Selection in Continual Learning with Small Buffers

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
arXiv AI
1d ago

Drift-adaptive Representation Preservation for Domain-Incremental Learning

The paper proposes a drift‑adaptive method for Domain‑Incremental Learning that tracks representation drift for each past domain‑class pair. By measuring how much a concept’s current representation deviates from a fixed anchor, the method assigns stronger preservation to high‑drift pairs and down‑weights highly drifted domains during prototype construction. Experiments on DomainNet, Office‑Home, CORe50, and CDDB‑Hard show that this approach maintains a strong accuracy‑forgetting balance while using less training memory and the smallest storage compared to other methods.

By Hyunmin Kong, Gyutae Oh, Jitae Shin