01553nas a2200133   4500008004100000245006800041210006700109260001300176520111900189100001901308700002301327700002001350856004901370       2022                        eng d00aExplaining Siamese Networks in Few-Shot Learning for Audio Data0 aExplaining Siamese Networks in FewShot Learning for Audio Data  bSpringer3 aMachine learning models are not able to generalize correctly when queried on samples belonging to class distributions that were never seen during training. This is a critical issue, since real world applications might need to quickly adapt without the necessity of re-training. To overcome these limitations, few-shot learning frameworks have been proposed and their applicability has been studied widely for computer vision tasks. Siamese Networks learn pairs similarity in form of a metric that can be easily extended on new unseen classes. Unfortunately, the downside of such systems is the lack of explainability. We propose a method to explain the outcomes of Siamese Networks in the context of few-shot learning for audio data. This objective is pursued through a local perturbation-based approach that evaluates segments-weighted-average contributions to the final outcome considering the interplay between different areas of the audio spectrogram. Qualitative and quantitative results demonstrate that our method is able to show common intra-class characteristics and erroneous reliance on silent sections.1 aFedele, Andrea1 aGuidotti, Riccardo1 aPedreschi, Dino  uhttps://doi.org/10.1007/978-3-031-18840-4_36