Combining GANs and AutoEncoders for Efficient Anomaly Detection
Deep learned models are now largely adopted in different fields, and they generally provide superior performances with respect to classical signal-based approaches. Notwithstanding this, their actual reliability when working in an unprotected environment is far enough to be proven...
In this work, we consider a novel deep neural network architecture, named Neural Ordinary Differential Equations (N-ODE), that is getting particular attention due to an attractive property — a test-time tunable trade-off between accuracy and efficiency. This paper analyzes the robustness of N-ODE image classifiers when faced against a strong adversarial attack and how its effectiveness changes