This paper presents an infinite variational autoencoder (VAE) whose capacity adapts to suit the input data. This is achieved using a mixture model where the mixing coefficients are modeled by a Dirichlet process, allowing us to integrate over the coefficients 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 flexibility of our method, particularly for semi-supervised learning, where only a small number of training samples are available.
For example, REF proposed to use a mixture of VAEs for semisupervised classification tasks, where the mixing coefficients of these VAEs are modeled by a Dirichlet process to adapt its capacity to the input data.
Infinite Variational Autoencoder for Semi-Supervised Learning
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Mathematics Computer Science