22 citations · 42 across the 14 of their papers we have counts for
4 papers · 1 filter
DAReN: A Collaborative Approach Towards Reasoning And Disentangling
Pritish Sahu, Kalliopi Basioti, Vladimir Pavlovic
Computational learning approaches to solving visual reasoning tests, such as Raven's Progressive Matrices (RPM), critically depend on the ability to identify the visual concepts us…
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…