12 mJ per Class On-Device Online Few-Shot Class-Incremental Learning

arXiv:2403.07851v1 Announce Type: new
Abstract: Few-Shot Class-Incremental Learning (FSCIL) enables machine learning systems to expand their inference capabilities to new classes using only a few labeled examples, without forgetting the previously learned classes. Classical backpropagation-based learning and its variants are often unsuitable for battery-powered, memory-constrained systems at the extreme edge. In this work, we introduce Online Few-Shot Class-Incremental Learning (O-FSCIL), based on a lightweight model consisting of a pretrained and metalearned feature extractor and an expandable explicit memory storing the …

abstract arxiv backpropagation battery capabilities class cs.cv cs.lg edge examples expand few-shot incremental inference learning systems machine machine learning memory per systems type variants