[1905.12724] Diffusion Variational Autoencoders

Variational autoencoders (VAEs) have become one of the most popular deep learning approaches to unsupervised learning and data generation. However, traditional VAEs suffer from the constraint that the latent space must distributionally match a simple prior (e.g. normal, uniform), independent of the initial data distribution. This leads to a number of issues around modeling manifold data, as there is no function with bounded Jacobian that maps a normal distribution to certain manifolds (e.g. sphere). Similarly, there are not many theoretical guarantees on the encoder and decoder created by the VAE. In this work, we propose a variational autoencoder that maps manifold valued data to its diffusion map coordinates in the latent space, resamples in a neighborhood around a given point in the latent space, and learns a decoder that maps the newly resampled points back to the manifold. The framework is built off of SpectralNet, and is capable of learning this data dependent latent space withou

Keywords: autoencoder
Date: 2019/06/05 09:47

Related Entries

Read more Deep Learningを用いた教師なし画像検査の論文調査 GAN/SVM/Autoencoderとか .pdf
0 users, 0 mentions 2018/10/09 06:53
Read more Variational Autoencoder in Tensorflow - facial expression low dimensional embedding - Machine learni...
0 users, 0 mentions 2018/04/22 03:40
Read more 時系列データでVariational AutoEncoder keras - 機械学習を学習する天然ニューラルネットワーク
0 users, 0 mentions 2018/09/23 06:23
Read more 猫でも分かるVariational AutoEncoder
0 users, 0 mentions 2018/06/15 04:30
Read more Intuitively Understanding Variational Autoencoders – Towards Data Science
0 users, 0 mentions 2018/07/08 12:23