Few-Shot Open-Set Audio Classification via Transductive Prototype Refinement and Class Logit Enhancement
Few-shot Open-set audio classification requires classifying query samples from known classes with a few labeled support samples while rejecting query samples from unknown classes. Transductive inference jointly observes the full unlabeled query set to improve prototype estimation, yet standard transductive updates do not distinguish known from unknown query samples, leaving prototypes vulnerable to open-set contamination. Drawing on latent-inlierness weighting and decoupled scoring for unknown-class samples, we propose a two-phase transductive method operating over a frozen audio encoder. First, each query sample is assigned a latent inlierness score that down-weights likely unknown-class...
| Authors | Tianyan Deng, Yanxiong Li, Rui Gao et al. |
|---|---|
| Primary category | cs.SD |
| Published | 29 July 2026 |
| Last updated at source | 24 August 2026 |
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