565 citations · 663 across the 57 of their papers we have counts for
3 papers · 1 filter
Unsupervised Source Separation via Bayesian Inference in the Latent Domain
Michele Mancusi, Emilian Postolache, Giorgio Mariani +4
State of the art audio source separation models rely on supervised data-driven approaches, which can be expensive in terms of labeling resources. On the other hand, approaches for…
Universal Spectral Adversarial Attacks for Deformable Shapes
Arianna Rampini, Franco Pestarini, Luca Cosmo +2
Machine learning models are known to be vulnerable to adversarial attacks, namely perturbations of the data that lead to wrong predictions despite being imperceptible. However, the…
Learning disentangled representations via product manifold projection
Marco Fumero, Luca Cosmo, Simone Melzi +1
We propose a novel approach to disentangle the generative factors of variation underlying a given set of observations. Our method builds upon the idea that the (unknown) low-dimens…