22 citations · 42 across the 14 of their papers we have counts for
4 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…
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…