arxivJul 3bullish

Few-Shot Open-Set Audio Classification Using Attention Information-Fused Prototypes

arXiv:2607.01297v1 Announce Type: cross Abstract: Most existing audio classification methods suppose that each query (testing) sample belongs to a class of support (training) samples, and misrecognize samples of unseen classes as seen classes (cannot reject samples of unseen classes). In this study,

RE1 model #audio-classification #few-shot #open-set Read on arxiv →

arxivJun 24bullish

Exploring Dualistic Meta-Learning to Enhance Domain Generalization in Open Set Scenarios

arXiv:2606.23758v1 Announce Type: cross Abstract: Domain generalization learns from multiple source domains to generalize to unseen target domains. However, it often neglects the realistic case of label mismatch between source and target. Open set domain generalization is then proposed to recognize

ME1 model #domain-generalization #meta-learning #open-set Read on arxiv →

arxivApr 24bullish

Energy-Based Open-Set Active Learning for Object Classification

arXiv:2604.20083v1 Announce Type: new Abstract: Active learning (AL) has emerged as a crucial methodology for minimizing labeling costs in deep learning by selecting the most valuable samples from a pool of unlabeled data for annotation. Traditional AL operates under a closed-set assumption, where a

ENEN2 models #active-learning #open-set #classification Read on arxiv →