Researchers propose SAMPLESELECT, a method for improving few-shot audio classification by predicting a feature mask for each input. This can help mitigate representation shift when foreground-background correlations change. The method is evaluated on ResNet12 and Conv64 models with significant improvements in out-of-distribution accuracy.