This is a joint work by Minyoung Kim, Pritish Sahu, Behnam Gholami, Vladimir Pavlovic.
For more information please visit: “https://seqam-lab.github.io/GPDA“
 M. Kim, P. Sahu, B. Gholami, and V. Pavlovic, “Unsupervised Visual Domain Adaptation: A Deep Max-Margin … Read the rest
Last week was the time for ICCV 2017 in Venice, Italy. Setting aside my personal indifference to Venice (overcrowded and overpriced, uninspiring food), ICCV was an interesting meeting. It took place on Lido, at the same venue as the Venice … Read the rest
Our paper “Unsupervised Domain Adaptation with Copula Models” was presented this week at the IEEE Int’l Workshop on Machine Learning for Signal Processing in Tokyo, Japan.
It was an exciting meeting; unlike the now mega-conferences of CVPR and … Read the rest
It was quite exciting to attend the largest CVPR ever – almost 5000 attendees. Having it in a beautiful location made it even more appealing.
Thanks to my students and colleagues who made the work we presented at CVPR possible.… Read the rest
Our paper about unsupervised probabilistic domain adaptation  for deep models (and other models too) has been accepted for ICCV’17:
 B. Gholami, O. Rudovic, and V. Pavlovic, “PUnDA: Probabilistic Unsupervised Domain Adaptation,” in Proc. IEEE International Conference
… Read the rest
We are excited to have three CVPR 2017 main conference papers accepted [1, 2, 3], as well as one workshop paper  :
 B. Babagholami and V. Pavlovic, “Probabilistic Temporal Subspace Clustering,” in
… Read the rest