  Clustering using deep neural network models have been extensively studied in
recent years. Among the most popular frameworks are the VAE and GAN frameworks,
which learns latent feature representations of data through encoder / decoder
neural net structures. This is a suitable base for clustering tasks, as the
latent space often seems to effectively capture the inherent essence of data,
simplifying its manifold and reducing noise. In this article, the VAE framework
is used to investigate how probability function gradient ascent over data
points can be used to process data in order to achieve better clustering.
Improvements in classification is observed comparing with unprocessed data,
although state of the art results are not obtained. Processing data with
gradient descent however results in more distinct cluster separation, making it
simpler to investigate suitable hyper parameter settings such as the number of
clusters. We propose a simple yet effective method for investigating suitable
number of clusters for data, based on the DBSCAN clustering algorithm, and
demonstrate that cluster number determination is facilitated with gradient
processing. As an additional curiosity, we find that our baseline model used
for comparison; a GMM on a t-SNE latent space for a VAE structure with weight
one on reconstruction during training (autoencoder), yield state of the art
results on the MNIST data, to our knowledge not beaten by any other existing
model.
