Collective dynamics of repeated inference in variational autoencoder rapidly find cluster structure.

ABSTRACT: Deep neural networks are good at extracting low-dimensional subspaces (latent spaces) that represent the essential features inside a high-dimensional dataset. Deep generative models represented by variational autoencoders (VAEs) can generate and infer high-quality datasets, such as images. In particular, VAEs can eliminate the noise contained in an image by repeating the mapping between latent and data space. To clarify the mechanism of such denoising, we numerically analyzed how the activity pattern of trained networks changes in the latent space during inference. We considered the time development of the activity pattern for specific data as one trajectory in the latent space and investigated the collective behavior of these inference trajectories for many data. Our study revealed that w

SUBMITTER: Nagano Y 

PROVIDER: S-EPMC7524732 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature