91 citations · 296 across the 23 of their papers we have counts for
7 papers · 1 filter
Identification of Attack-Specific Signatures in Adversarial Examples
Hossein Souri, Pirazh Khorramshahi, Chun Pong Lau +2
The adversarial attack literature contains a myriad of algorithms for crafting perturbations which yield pathological behavior in neural networks. In many cases, multiple algorithm…
To Boost or not to Boost: On the Limits of Boosted Neural Networks
Sai Saketh Rambhatla, Michael Jones, Rama Chellappa
Boosting is a method for finding a highly accurate hypothesis by linearly combining many ``weak" hypotheses, each of which may be only moderately accurate. Thus, boosting is a meth…
cGANs with Multi-Hinge Loss
Ilya Kavalerov, Wojciech Czaja, Rama Chellappa
We propose a new algorithm to incorporate class conditional information into the critic of GANs via a multi-class generalization of the commonly used Hinge loss that is compatible…
Invert and Defend: Model-based Approximate Inversion of Generative Adversarial Networks for Secure Inference
Wei-An Lin, Yogesh Balaji, Pouya Samangouei +1
Inferring the latent variable generating a given test sample is a challenging problem in Generative Adversarial Networks (GANs). In this paper, we propose InvGAN - a novel framewor…
Normalized Wasserstein Distance for Mixture Distributions with Applications in Adversarial Learning and Domain Adaptation
Yogesh Balaji, Rama Chellappa, Soheil Feizi
Understanding proper distance measures between distributions is at the core of several learning tasks such as generative models, domain adaptation, clustering, etc. In this work, w…
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs
Yogesh Balaji, Hamed Hassani, Rama Chellappa +1
Building on the success of deep learning, two modern approaches to learn a probability model from the data are Generative Adversarial Networks (GANs) and Variational AutoEncoders (…