Topic
few-shot learning
Artificial Intelligence #few-shot learning#class-variable incremental
Prototype Adaptation and Pseudo Class-Variable Training Boost Few-Shot Audio Classification
Researchers propose a method for few-shot class-variable incremental audio classification, handling both increases and decreases in the number of classes. The approach uses a prototype adaptation network and pseudo class-variable training. Experiments on three public datasets show improved average accuracy over previous methods.
Jun 17, 2026 1 source
Artificial Intelligence #few-shot learning#biomedical relation extraction
Few-Shot Biomedical Relation Extraction with LLMs: A Viable Alternative to Supervised Learning?
A new study on arXiv investigates few-shot biomedical relation extraction using large language models (LLMs). The best model achieved micro-F1 of 0.44, surpassing prior few-shot results but below supervised baseline. However, on macro-F1, prompt-based methods outperformed supervised learning, particularly on rare relation types, highlighting LLMs' potential in low-resource settings.
Jun 16, 2026 1 source