@conference {1568,
	title = {Explain and Interpret Few-Shot Learning},
	booktitle = {Joint Proceedings of the xAI-2023 Late-breaking Work, Demos and Doctoral Consortium co-located with the 1st World Conference on eXplainable Artificial Intelligence (xAI-2023), Lisbon, Portugal, July 26-28, 2023},
	year = {2023},
	publisher = {CEUR-WS.org},
	organization = {CEUR-WS.org},
	abstract = {Recent 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 models},
	url = {https://ceur-ws.org/Vol-3554/paper38.pdf},
	author = {Andrea Fedele}
}
