2 citations · 5 across the 5 of their papers we have counts for
6 papers
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…
Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization
Federico Fontana, Anxhelo Diko, Romeo Lanzino +4
The rapid evolution of deepfake generation technologies poses critical challenges for detection systems, as non-continual learning methods demand frequent and expensive retraining.…
CycleBNN: Cyclic Precision Training in Binary Neural Networks
Federico Fontana, Romeo Lanzino, Anxhelo Diko +2
This paper works on Binary Neural Networks (BNNs), a promising avenue for efficient deep learning, offering significant reductions in computational overhead and memory footprint to…
NT-ViT: Neural Transcoding Vision Transformers for EEG-to-fMRI Synthesis
Romeo Lanzino, Federico Fontana, Luigi Cinque +2
This paper introduces the Neural Transcoding Vision Transformer (\modelname), a generative model designed to estimate high-resolution functional Magnetic Resonance Imaging (fMRI) s…
Semantically Guided Representation Learning For Action Anticipation
Anxhelo Diko, Danilo Avola, Bardh Prenkaj +2
Action anticipation is the task of forecasting future activity from a partially observed sequence of events. However, this task is exposed to intrinsic future uncertainty and the d…
Faster Than Lies: Real-time Deepfake Detection using Binary Neural Networks
Lanzino Romeo, Fontana Federico, Diko Anxhelo +2
Deepfake detection aims to contrast the spread of deep-generated media that undermines trust in online content. While existing methods focus on large and complex models, the need f…