# Performance Analysis of Semi-supervised Learning in the Small-data Regime using VAEs

arXiv: https://arxiv.org/abs/2002.12164 · HTML: https://mlsanity.com/paper/2002.12164/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2020.

## Built on by (most cited first, 4 of 4)

- [Deep learning-based super-resolution fluorescence microscopy on small datasets](https://mlsanity.com/paper/2103.04989/) (2021)
- [Convolutional Neural Network Denoising in Fluorescence Lifetime Imaging Microscopy (FLIM)](https://mlsanity.com/paper/2103.05448/) (2021)
- [Low dosage 3D volume fluorescence microscopy imaging using compressive sensing](https://mlsanity.com/paper/2201.00820/) (2022)
- [Low-Energy Convolutional Neural Networks (CNNs) using Hadamard Method](https://mlsanity.com/paper/2209.09106/) (2022)

## Abstract (arXiv)

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.