Reference-based Variational Autoencoders
Learning disentangled representations from visual data, where different high-level generative factors are independently encoded, is of importance for many computer vision tasks. Solving this problem, however, typically requires to explicitly label all the factors of interest in training images. To alleviate the annotation cost, we introduce a learning setting which we refer to as \textit{reference-based disentangling}. Given a pool of unlabelled images, the goal is to learn a representation where a set of target factors are disentangled from others. The only supervision comes from an auxiliary \textit{reference set} containing images where the factors of interest are constant. To address this problem, we propose reference-based variational autoencoders, a novel deep generative model designed to exploit the weak-supervision provided by the reference set. By addressing tasks such as feature learning, conditional image generation or attribute transfer, we validate the ability of the proposed model to learn disentangled representations from this minimal form of supervision.
Code (0)
등록된 구현이 없습니다.
Tasks
AttributeConditional Image GenerationImage GenerationSimilar Papers 제목 키워드 기반
Sequential Variational Autoencoders for Collaborative Filtering
Variational autoencoders were proven successful in domains such as computer vision and speech processing. Their adoption for modeling user preferences is still unexplored, although recently it is starting to gain attenti…
Recommendation SystemsJoint Variational Autoencoders for Recommendation with Implicit Feedback
Variational Autoencoders (VAEs) have recently shown promising performance in collaborative filtering with implicit feedback. These existing recommendation models learn user representations to reconstruct or predict user …
Collaborative FilteringLearning Disentangled Representations with Reference-Based Variational Autoencoders
Learning disentangled representations from visual data, where different high-level generative factors are independently encoded, is of importance for many computer vision tasks. Solving this problem, however, typically r…
AttributeConditional Image GenerationImage GenerationThe advantage of using Student's t-priors in variational autoencoders
Is it optimal to use the standard Gaussian prior in variational autoencoders? With Gaussian distributions, which are not weakly informative priors, variational autoencoders struggle to reconstruct the actual data. We pro…
An Introduction to Variational Autoencoders
Variational autoencoders provide a principled framework for learning deep latent-variable models and corresponding inference models. In this work, we provide an introduction to variational autoencoders and some important…