no code implementations • 29 Sep 2021 • Behnam Gholami, Mostafa El-Khamy, Kee-Bong Song
We demonstrate the effectiveness of our approach on several widely used datasets for the domain generalization problem, on all of which we achieve competitive results with state-of-the-art models.
no code implementations • 26 Sep 2019 • Behnam Gholami, Pritish Sahu, Minyoung Kim, Vladimir Pavlovic
In this paper, we improve the performance of DA by introducing a discriminative discrepancy measure which takes advantage of auxiliary information available in the source and the target domains to better align the source and target distributions.
1 code implementation • CVPR 2019 • Minyoung Kim, Pritish Sahu, Behnam Gholami, Vladimir Pavlovic
The latter can be achieved by minimizing the maximum discrepancy of predictors (classifiers).
Ranked #3 on Synthetic-to-Real Translation on Syn2Real-C
no code implementations • ICLR 2019 • Behnam Gholami, Pritish Sahu, Ognjen Rudovic, Konstantinos Bousmalis, Vladimir Pavlovic
Unsupervised domain adaptation (uDA) models focus on pairwise adaptation settings where there is a single, labeled, source and a single target domain.
no code implementations • ICCV 2017 • Behnam Gholami, Ognjen (Oggi) Rudovic, Vladimir Pavlovic
This paper introduces a probabilistic latent variable model to address unsupervised domain adaptation problems.
no code implementations • CVPR 2017 • Behnam Gholami, Vladimir Pavlovic
In this paper, we propose a unified non-parametric generative framework for temporal subspace clustering to segment data drawn from a sequentially ordered union of subspaces that deals with the missing features in a principled way.