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- W4386914863 abstract "<p>Data augmentation represents an opportunity for Artificial Intelligence applications, as it aims at creating new synthetic data based on an existing baseline. In this paper, we present a new evaluation framework for Generative Adversarial Networks (GANs), a data augmentation technique, in multivariate data classification contexts. The goal is not limited to assess the performance variations obtained through GANs, but also to inspect results with explainable AI (XAI) tools, understanding how GANs work and, finally, exploiting them to discover new knowledge. To this aim, we adopt the Logic Learning Machine for performance assessment and rule extraction, and introduce a new measure of rule similarity to compare different artificial datasets. We apply the methodology on two case studies, activity recognition and physical fatigue detection, confirming that GANs can help in overcoming limitations of original datasets and lead to new discoveries. </p>" @default.
- W4386914863 created "2023-09-22" @default.
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- W4386914863 date "2023-09-21" @default.
- W4386914863 modified "2023-10-16" @default.
- W4386914863 title "Explainable Evaluation of Generative Adversarial Networks For Wearables Data Augmentation" @default.
- W4386914863 doi "https://doi.org/10.36227/techrxiv.24166791.v1" @default.
- W4386914863 hasPublicationYear "2023" @default.
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