Portrait de Francesco Paissan

Francesco Paissan

Visiteur de recherche indépendant
Superviseur⋅e principal⋅e
Cem Subakan
Sujets de recherche
Apprentissage automatique pour la parole et l'audio
Apprentissage profond
Interprétabilité
Réseaux de neurones
Traitement audio
Site web
Google Scholar
GitHub

Publications

Posthoc Interpretation via Quantization
Cem Subakan
Francesco Paissan
Mirco Ravanelli
In this paper, we introduce a new approach, called Posthoc Interpretation via Quantization (PIQ), for interpreting decisions made by trained… (voir plus) classifiers. Our method utilizes vector quantization to transform the representations of a classifier into a discrete, class-specific latent space. The class-specific codebooks act as a bottleneck that forces the interpreter to focus on the parts of the input data deemed relevant by the classifier for making a prediction. Our model formulation also enables learning concepts by incorporating the supervision of pretrained annotation models such as state-of-the-art image segmentation models. We evaluated our method through quantitative and qualitative studies involving black-and-white images, color images, and audio. As a result of these studies we found that PIQ generates interpretations that are more easily understood by participants to our user studies when compared to several other interpretation methods in the literature.
2023-03-22
ArXiv (prépublication)
doi.org
arxiv.org