43 citations · 132 across the 28 of their papers we have counts for
7 papers · 1 filter
Conditioning Trick for Training Stable GANs
Mohammad Esmaeilpour, Raymel Alfonso Sallo, Olivier St-Georges +2
In this paper we propose a conditioning trick, called difference departure from normality, applied on the generator network in response to instability issues during GAN training. W…
Deep DA for Ordinal Regression of Pain Intensity Estimation Using Weakly-Labeled Videos
Gnana Praveen R, Eric Granger, Patrick Cardinal
Automatic estimation of pain intensity from facial expressions in videos has an immense potential in health care applications. However, domain adaptation (DA) is needed to alleviat…
Class-Conditional Defense GAN Against End-to-End Speech Attacks
Mohammad Esmaeilpour, Patrick Cardinal, Alessandro Lameiras Koerich
In this paper we propose a novel defense approach against end-to-end adversarial attacks developed to fool advanced speech-to-text systems such as DeepSpeech and Lingvo. Unlike con…
Improving Stability of LS-GANs for Audio and Speech Signals
Mohammad Esmaeilpour, Raymel Alfonso Sallo, Olivier St-Georges +2
In this paper we address the instability issue of generative adversarial network (GAN) by proposing a new similarity metric in unitary space of Schur decomposition for 2D represent…
Adversarially Training for Audio Classifiers
Raymel Alfonso Sallo, Mohammad Esmaeilpour, Patrick Cardinal
In this paper, we investigate the potential effect of the adversarially training on the robustness of six advanced deep neural networks against a variety of targeted and non-target…
Deep Domain Adaptation for Ordinal Regression of Pain Intensity Estimation Using Weakly-Labelled Videos
R. Gnana Praveen, Eric Granger, Patrick Cardinal
Estimation of pain intensity from facial expressions captured in videos has an immense potential for health care applications. Given the challenges related to subjective variations…