4 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…