57 citations · 121 across the 9 of their papers we have counts for
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cs.LG2019★ 2 cited
Adversarial Robustness of Flow-Based Generative Models
Phillip Pope, Yogesh Balaji, Soheil Feizi
Flow-based generative models leverage invertible generator functions to fit a distribution to the training data using maximum likelihood. Despite their use in several application d…
cs.LG2019★ 5 cited
Learning a Domain-Invariant Embedding for Unsupervised Domain Adaptation Using Class-Conditioned Distribution Alignment
Alex Gabourie, Mohammad Rostami, Philip Pope +2
We address the problem of unsupervised domain adaptation (UDA) by learning a cross-domain agnostic embedding space, where the distance between the probability distributions of the…