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arXiv · 2002.12164

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

Varun Mannam·Arman Kazemi
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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.

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Varun Mannam, Arman Kazemi. 2020-02-26. Performance Analysis of Semi-supervised Learning in the Small-data Regime using VAEs. https://arxiv.org/abs/2002.12164

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