Dropout regularization has been widely used in various deep neural networks to combat overfitting. It works by training a network to be more robust on information-degraded data points for better generalization. Conventional dropout and variants are …
Adversarial examples induce model classification errors on purpose, which has raised concerns on the security aspect of machine learning techniques. Many existing countermeasures are compromised by adaptive adversaries and transferred examples. We …
Pattern recognition is an essential part of modern security systems for malware detection, intrusion detection, and spam filtering. Conventional classifiers widely used in these applications are found vulnerable themselves to adversarial machine …
Due to large heterogeneity gaps between image, text, and video, finding content similarities of multimedia data is a challenging problem yet to be resolved. In this paper, we propose to integrate high-level feature extractions and learning of the …