3 papers
cs.LG2026
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…
cs.LG2026
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…
cs.LG2025
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…