3 citations · 5 across the 5 of their papers we have counts for
5 papers
Empty SPACE: Cross-Attention Sparsity for Concept Erasure in Diffusion Models
Nicola Novello, Andrea M. Tonello
Erasing specific concepts from text-to-image diffusion models is essential for avoiding the generation of copyrighted and explicit content. Closed-form concept erasure methods offe…
A Unified Framework for Diffusion Model Unlearning with f-Divergence
Nicola Novello, Federico Fontana, Luigi Cinque +2
Most existing methods for concept unlearning in text-to-image diffusion models minimize a mean squared error (MSE) loss between the denoiser outputs conditioned on a target and an…
Robust Classification with Noisy Labels Based on Posterior Maximization
Nicola Novello, Andrea M. Tonello
Designing objective functions robust to label noise is crucial for real-world classification algorithms. In this paper, we investigate the robustness to label noise of an -diver…
-Divergence Based Classification: Beyond the Use of Cross-Entropy
Nicola Novello, Andrea M. Tonello
In deep learning, classification tasks are formalized as optimization problems often solved via the minimization of the cross-entropy. However, recent advancements in the design of…
Mutual Information Estimation via -Divergence and Data Derangements
Nunzio A. Letizia, Nicola Novello, Andrea M. Tonello
Estimating mutual information accurately is pivotal across diverse applications, from machine learning to communications and biology, enabling us to gain insights into the inner me…