{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/performance-analysis-of-semi-supervised","title":"Performance Analysis of Semi-supervised Learning in the Small-data Regime using VAEs","arxiv_id":"2002.12164","date":"2020-02-26","proceeding":null,"authors":["Varun Mannam","Arman Kazemi"],"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.","url_abs":"https://arxiv.org/abs/2002.12164v1","url_pdf":"https://arxiv.org/pdf/2002.12164v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"performance-analysis-of-semi-supervised","repo_url":"https://github.com/varunmannam/Papers_with_Code/tree/master/VAE_project","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"small-data","task_name":"Small Data Image Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/small-data-image-classification-on-cifar10-10","task":"Small Data Image Classification","dataset":"cifar10, 10 labels","model":"VAE","rank_in_archive_order":1,"of":2,"metrics":{"% Test Accuracy":"45.96%"},"uses_additional_data":false},{"leaderboard":"/sota/small-data-image-classification-on-cifar10-10","task":"Small Data Image Classification","dataset":"cifar10, 10 labels","model":"VAE","rank_in_archive_order":2,"of":2,"metrics":{"% Test Accuracy":"45.96%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}