Multi-label Few-shot Learning for Sound Event Recognition

Few-shot classification aims to generalize the concept from seen classes to unseen novel classes using only a few examples. Although significant progress in few-shot classification has been made, most approaches focus on a standard multi-class scenario and are based on learning single-label embedding of the labeled examples to classify the unlabeled examples. Besides, we note that state-of-the-art methods in few-shot learning mostly adopt a metric-based architecture and the the so-called episode training strategy. While this approach works nicely for multiclass classification, it is hard to apply it to the multi-label scenario because of the complexity of forming an episode. In this paper, we propose a One-vs.-Rest episode selection strategy to mitigate this issue and apply the strategy to the multi-label few-shot problem. Experiments conducted using the large-scale data found in the AudioSet show that the models with our training strategy extract the semantic features under the multi-label setting.