  Extracting large amounts of data from biological samples is not feasible due
to radiation issues, and image processing in the small-data regime is one of
the critical challenges when working with a limited amount of data. In this
work, we applied an existing algorithm named Variational Auto Encoder (VAE)
that pre-trains a latent space representation of the data to capture the
features in a lower-dimension for the small-data regime input. The fine-tuned
latent space provides constant weights that are useful for classification. Here
we will present the performance analysis of the VAE algorithm with different
latent space sizes in the semi-supervised learning using the CIFAR-10 dataset.
