Enhancing variational generation through self-decomposition

TL;DR

This paper introduces Split Variational Autoencoders (SVAE), which decompose generated images into meaningful components using learned maps, improving generation quality and interpretability without extra loss functions.

Contribution

The paper proposes a novel SVAE model that automatically decomposes images into components via learned maps, enhancing generative performance without additional training constraints.

Findings

SVAE outperforms previous variational models on MNIST, CIFAR-10, and CelebA.

Decomposition schemes can be syntactic or semantic, affecting image quality.

The method improves FID scores by encouraging meaningful image splits.

Abstract

In this article we introduce the notion of Split Variational Autoencoder (SVAE), whose output $\hat{x}$ is obtained as a weighted sum $\sigma \odot \hat{x_1} + (1-\sigma) \odot \hat{x_2}$ of two generated images $\hat{x_1},\hat{x_2}$, and $\sigma$ is a {\em learned} compositional map. The composing images $\hat{x_1},\hat{x_2}$, as well as the $\sigma$-map are automatically synthesized by the model. The network is trained as a usual Variational Autoencoder with a negative loglikelihood loss between training and reconstructed images. No additional loss is required for $\hat{x_1},\hat{x_2}$ or $\sigma$, neither any form of human tuning. The decomposition is nondeterministic, but follows two main schemes, that we may roughly categorize as either \say{syntactic} or \say{semantic}. In the first case, the map tends to exploit the strong correlation between adjacent pixels, splitting the image in two complementary high frequency sub-images. In the second case, the map typically focuses on the contours of objects, splitting the image in interesting variations of its content, with more marked and distinctive features. In this case, according to empirical observations, the Fr\'echet Inception Distance (FID) of $\hat{x_1}$ and $\hat{x_2}$ is usually lower (hence better) than that of $\hat{x}$, that clearly suffers from being the average of the former. In a sense, a SVAE forces the Variational Autoencoder to make choices, in contrast with its intrinsic tendency to {\em average} between alternatives with the aim to minimize the reconstruction loss towards a specific sample. According to the FID metric, our technique, tested on typical datasets such as Mnist, Cifar10 and CelebA, allows us to outperform all previous purely variational architectures (not relying on normalization flows).