Semi-supervised Learning with Deep Generative Models
نویسندگان
چکیده
The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis. We revisit the approach to semi-supervised learning with generative models and develop new models that allow for effective generalisation from small labelled data sets to large unlabelled ones. Generative approaches have thus far been either inflexible, inefficient or non-scalable. We show that deep generative models and approximate Bayesian inference exploiting recent advances in variational methods can be used to provide significant improvements, making generative approaches highly competitive for semi-supervised learning.
منابع مشابه
Auxiliary Deep Generative Models
Deep generative models parameterized by neural networks have recently achieved state-ofthe-art performance in unsupervised and semisupervised learning. We extend deep generative models with auxiliary variables which improves the variational approximation. The auxiliary variables leave the generative model unchanged but make the variational distribution more expressive. Inspired by the structure...
متن کاملHybrid Deep Discriminative/Generative Models for Semi-Supervised Learning
Most methods in machine learning are described as either discriminative or generative. The former often attain higher predictive accuracy, while the latter are more strongly regularized and can deal with missing data. Here, we propose a new framework to combine a broad class of discriminative and generative models, interpolating between the two extremes with a multiconditional likelihood object...
متن کاملMax-Margin Deep Generative Models for (Semi-)Supervised Learning
Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability. However, it is relatively insufficient to empower the discriminative ability of DGMs on making accurate predictions. This paper presents max-margin deep generative models (mmDGMs) and a class-conditional variant (mmDCGMs...
متن کاملImproving Semi-Supervised Learning with Auxiliary Deep Generative Models
Abstract Deep generative models based upon continuous variational distributions parameterized by deep networks give state-of-the-art performance. In this paper we propose a framework for extending the latent representation with extra auxiliary variables in order to make the variational distribution more expressive for semi-supervised learning. By utilizing the stochasticity of the auxiliary var...
متن کاملInverting VAEs for Improved Generative Accuracy
Recent advances in semi-supervised learning with deep generative models have shown promise in generalizing from small labeled datasets (xl,yl) to large unlabeled ones (xu). When the codomain (y) has known structure, a large unfeatured dataset (yu) is potentially available. We develop a parameter-efficient, deep semisupervised generative model for the purpose of exploiting this untapped data sou...
متن کامل