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Found 7 papers, 1 papers with code
On the Properties of Adversarially-Trained CNNs
Adversarial Training has proved to be an effective training paradigm to enforce robustness against adversarial examples in modern neural network architectures.
Improving Robustness with Image Filtering
By leveraging the IGE representation, we build a new defense method, Filtering As a Defense, that does not allow the attacker to entangle pixels to create malicious patterns.
$β$-Variational Classifiers Under Attack
Deep Neural networks have gained lots of attention in recent years thanks to the breakthroughs obtained in the field of Computer Vision.
IntroVAC: Introspective Variational Classifiers for Learning Interpretable Latent Subspaces
Learning useful representations of complex data has been the subject of extensive research for many years.
Adversarial Training Reduces Information and Improves Transferability
Recent results show that features of adversarially trained networks for classification, in addition to being robust, enable desirable properties such as invertibility.
Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance
Anomaly Detection is an unsupervised learning task aimed at detecting anomalous behaviours with respect to historical data.
Directional Adversarial Training for Cost Sensitive Deep Learning Classification Applications
Adversarial Training is a training procedure aiming at providing models that are robust to worst-case perturbations around predefined points.
