Bidirectional Variational Autoencoders

Bart Kosko, Olaoluwa Adigun

Abstract:

We present the new bidirectional variational autoencoder (BVAE) network architecture. The BVAE uses a single
neural network both to encode and decode instead of an encoder-decoder network pair. The network encodes in the forward
direction and decodes in the backward direction through the
same synaptic web. Simulations compared BVAEs and ordinary VAEs on the four image tasks of image reconstruction,
classification, interpolation, and generation. The image datasets
included MNIST handwritten digits, Fashion-MNIST, CIFAR10, and CelebA-64 face images. The bidirectional structure of
BVAEs cut the parameter count by almost 50% and still slightly
outperformed the unidirectional VAEs