Papers › Performance Analysis of Semi-supervised Learning in the Small-data Regime using VAEs
Performance Analysis of Semi-supervised Learning in the Small-data Regime using VAEs
Varun Mannam, Arman Kazemi
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Small Data Image Classification | cifar10, 10 labels | VAE | % Test Accuracy | 45.96% | #1 of 2 | Archive leaderboard | report |
| Small Data Image Classification | cifar10, 10 labels | VAE | % Test Accuracy | 45.96% | #2 of 2 | Archive leaderboard | report |
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