Performance Analysis of Semi-supervised Learning in the Small-data Regime using VAEs
Machine Learning
2020-07-21 v2 Image and Video Processing
Machine Learning
Abstract
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.
Cite
@article{arxiv.2002.12164,
title = {Performance Analysis of Semi-supervised Learning in the Small-data Regime using VAEs},
author = {Varun Mannam and Arman Kazemi},
journal= {arXiv preprint arXiv:2002.12164},
year = {2020}
}