Sample-Conditioned Representation Selection for Audio Few-Shot Learning

2026-09-15

AI Summary

本文针对少样本音频分类中的前景-背景共现依赖问题，提出SAMPLESELECT方法，通过预测特征掩码来提高跨背景的泛化能力。

Abstract

Few-shot audio classifiers may rely on foreground-background co-occurrences and fail when those correlations shift. On SpurAudio, the resulting representation shift is concentrated and class dependent: for ResNet12, the top 10 percent of channels explain 82.80 percent of the null-corrected shift contribution. We propose SAMPLESELECT, which predicts a fixed-budget feature mask independently for each input while keeping the encoder and source classifier frozen. Training uses differentiable Gumbel Top-k selection with foreground classification and cross-background contrastive losses; inference uses deterministic Top-k masks and support-only linear adaptation. Across ResNet12 and Conv64 in 5-way 1-shot and 5-shot evaluation, SAMPLESELECT gives the best OOD accuracy among the compared methods and improves the matched full-representation control by 4.90-8.38 percentage points. Ablations and representation analyses further support the learned selection mechanism. Code is available at https://github.com/Cross-Innovation-Lab/SAMPLESELECT/

Problem

• few-shot learning
• audio classification
• representation shift
• foreground-background co-occurrences
• feature selection

Innovation

• SAMPLESELECT
• few-shot learning
• audio classification
• representation selection
• Gumbel Top-k