Performance Analysis of Semi-supervised Learning in the Small-data Regime using VAEs
This work addresses data scarcity issues in biological imaging, but it is incremental as it applies an existing method to a new domain without major innovations.
The study tackled the challenge of image processing with limited data by applying Variational Autoencoders (VAEs) to pre-train latent space representations for semi-supervised learning, achieving performance analysis on the CIFAR-10 dataset with varying latent space sizes.
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.