Branched Variational Autoencoder Classifiers
Ahmed Salah David Yevick
This paper introduces a modified variational autoencoder (VAEs) that contains
an additional neural network branch. The resulting branched VAE (BVAE)
contributes a classification component based on the class labels to the total
loss and therefore imparts categorical information to the latent
representation. As a result, the latent space distributions of the input
classes are separated and ordered, thereby enhancing the classification
accuracy. The degree of improvement is quantified by numerical calculations
employing the benchmark MNIST dataset for both unrotated and rotated digits.
The proposed technique is then compared to and then incorporated into a VAE
with fixed output distributions. This procedure is found to yield improved
performance for a wide range of output distributions.