01287nas a2200109   4500008004100000245004400041210004300085260001600128520096900144100001901113856004501132       2023                        eng d00aExplain and Interpret Few-Shot Learning0 aExplain and Interpret FewShot Learning  bCEUR-WS.org3 aRecent advancements in Artificial Intelligence have been fueled by vast datasets, powerful computing resources, and sophisticated algorithms. However, traditional Machine Learning models face limitations in handling scarce data. Few-Shot Learning (FSL) offers a promising solution by training models on a small number of examples per class. This manuscript introduces FXI-FSL, a framework for eXplainability and Interpretability in FSL, which aims to develop post-hoc explainability algorithms and interpretableby-design alternatives. A noteworthy contribution is the SIamese Network EXplainer (SINEX), a post-hoc approach shedding light on Siamese Network behavior. The proposed framework seeks to unveil the rationale behind FSL models, instilling trust in their real-world applications. Moreover, it emerges as a safeguard for developers, facilitating models fine-tuning prior to deployment, and as a guide for end users navigating the decisions of these models1 aFedele, Andrea  uhttps://ceur-ws.org/Vol-3554/paper38.pdf01553nas 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