资源分类
》 资源 》论文》Infinite Variational Autoencoder for Semi-Supervised Learning

Infinite Variational Autoencoder for Semi-Supervised Learning

2019-12-06 | |  104 |   50 |   0

Abstract

This paper presents an infifinite variational autoencoder (VAE) whose capacity adapts to suit the input data. This is achieved using a mixture model where the mixing coef- fificients are modeled by a Dirichlet process, allowing us to integrate over the coeffificients when performing inference. Critically, this then allows us to automatically vary the number of autoencoders in the mixture based on the data. Experiments show the flflexibility of our method, particularly for semi-supervised learning, where only a small number of training samples are available

上一篇：Deep Self-Taught Learning for Weakly Supervised Object Localization

下一篇：Semi-Supervised Deep Learning for Monocular Depth Map Prediction

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...