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Exploring the Task-agnostic Trait of Self-supervised Learning in the Context of Detecting Mental Disorders
March 25, 2024, 4:41 a.m. | Rohan Kumar Gupta, Rohit Sinha
cs.LG updates on arXiv.org arxiv.org
Abstract: Self-supervised learning (SSL) has been investigated to generate task-agnostic representations across various domains. However, such investigation has not been conducted for detecting multiple mental disorders. The rationale behind the existence of a task-agnostic representation lies in the overlapping symptoms among multiple mental disorders. Consequently, the behavioural data collected for mental health assessment may carry a mixed bag of attributes related to multiple disorders. Motivated by that, in this study, we explore a task-agnostic representation derived …
abstract arxiv context cs.ai cs.lg domains eess.sp generate however investigation lies multiple representation self-supervised learning ssl supervised learning type
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