4 papers · 1 filter
Angular Distance Distribution Loss for Audio Classification
Antonio Almudévar, Romain Serizel, Alfonso Ortega
Classification is a pivotal task in deep learning not only because of its intrinsic importance, but also for providing embeddings with desirable properties in other tasks. To optim…
Rethinking Disentanglement under Dependent Factors of Variation
Antonio Almudévar, Alfonso Ortega
Representation learning is an approach that allows to discover and extract the factors of variation from the data. Intuitively, a representation is said to be disentangled if it se…
Predefined Prototypes for Intra-Class Separation and Disentanglement
Antonio Almudévar, Théo Mariotte, Alfonso Ortega +4
Prototypical Learning is based on the idea that there is a point (which we call prototype) around which the embeddings of a class are clustered. It has shown promising results in s…
Explainable by-design Audio Segmentation through Non-Negative Matrix Factorization and Probing
Martin Lebourdais, Théo Mariotte, Antonio Almudévar +2
Audio segmentation is a key task for many speech technologies, most of which are based on neural networks, usually considered as black boxes, with high-level performances. However,…