Exploring the Task-agnostic Trait of Self-supervised Learning in the Context of Detecting Mental Disorders

arXiv:2403.15170v1 Announce Type: new
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