14 citations · 14 across the 5 of their papers we have counts for
8 papers
On the Properties of Adversarially-Trained CNNs
Mattia Carletti, Matteo Terzi, Gian Antonio Susto
Adversarial Training has proved to be an effective training paradigm to enforce robustness against adversarial examples in modern neural network architectures. Despite many efforts…
Improving Robustness with Image Filtering
Matteo Terzi, Mattia Carletti, Gian Antonio Susto
Adversarial robustness is one of the most challenging problems in Deep Learning and Computer Vision research. All the state-of-the-art techniques require a time-consuming procedure…
IntroVAC: Introspective Variational Classifiers for Learning Interpretable Latent Subspaces
Marco Maggipinto, Matteo Terzi, Gian Antonio Susto
Learning useful representations of complex data has been the subject of extensive research for many years. With the diffusion of Deep Neural Networks, Variational Autoencoders have…
-Variational Classifiers Under Attack
Marco Maggipinto, Matteo Terzi, Gian Antonio Susto
Deep Neural networks have gained lots of attention in recent years thanks to the breakthroughs obtained in the field of Computer Vision. However, despite their popularity, it has b…
Adversarial Training Reduces Information and Improves Transferability
Matteo Terzi, Alessandro Achille, Marco Maggipinto +1
Recent results show that features of adversarially trained networks for classification, in addition to being robust, enable desirable properties such as invertibility. The latter p…
Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance
Mattia Carletti, Matteo Terzi, Gian Antonio Susto
Anomaly Detection is an unsupervised learning task aimed at detecting anomalous behaviours with respect to historical data. In particular, multivariate Anomaly Detection has an imp…