<p dir="ltr">Traditional methods of behavior classification on videos of mice often rely on manually annotated datasets, which can be labor-intensive and resource-demanding to create. This research aims to address the challenges of behavior classification in mouse studies by leveraging an algorithmic framework employing self-supervised learning techniques capable of analyzing unlabeled datasets. This research seeks to develop a novel approach that eliminates the need for extensive manual annotation, making behavioral analysis more accessible and cost-effective for researchers, especially those in laboratories with limited access to annotated datasets.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/25686360 |
| Date | 27 April 2024 |
| Creators | Sruthi Sundharram (18437772) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/_b_MOUSE_SOCIAL_BEHAVIOR_CLASSIFICATION_USING_SELF-SUPERVISED_LEARNING_TECHNIQUES_b_/25686360 |
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