23 citations · 109 across the 19 of their papers we have counts for
6 papers · 1 filter
Task-Discriminative Domain Alignment for Unsupervised Domain Adaptation
Behnam Gholami, Pritish Sahu, Minyoung Kim +1
Domain Adaptation (DA), the process of effectively adapting task models learned on one domain, the source, to other related but distinct domains, the targets, with no or minimal re…
Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement
Minyoung Kim, Yuting Wang, Pritish Sahu +1
We propose a family of novel hierarchical Bayesian deep auto-encoder models capable of identifying disentangled factors of variability in data. While many recent attempts at factor…
Efficient Deep Gaussian Process Models for Variable-Sized Input
Issam H. Laradji, Mark Schmidt, Vladimir Pavlovic +1
Deep Gaussian processes (DGP) have appealing Bayesian properties, can handle variable-sized data, and learn deep features. Their limitation is that they do not scale well with the…
Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach
Minyoung Kim, Pritish Sahu, Behnam Gholami +1
In unsupervised domain adaptation, it is widely known that the target domain error can be provably reduced by having a shared input representation that makes the source and target…
Relevance Factor VAE: Learning and Identifying Disentangled Factors
Minyoung Kim, Yuting Wang, Pritish Sahu +1
We propose a novel VAE-based deep auto-encoder model that can learn disentangled latent representations in a fully unsupervised manner, endowed with the ability to identify all mea…
Online Multi-Object Tracking with Dual Matching Attention Networks
Ji Zhu, Hua Yang, Nian Liu +3
In this paper, we propose an online Multi-Object Tracking (MOT) approach which integrates the merits of single object tracking and data association methods in a unified framework t…