Exploring the Task-agnostic Trait of Self-supervised Learning in the
  Context of Detecting Mental Disorders
Rohan Kumar Gupta Rohit Sinha
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 through SSL in the context of
detecting major depressive disorder (MDD) and post-traumatic stress disorder
(PTSD) using audio and video data collected during interactive sessions. This
study employs SSL models trained by predicting multiple fixed targets or masked
frames. We propose a list of fixed targets to make the generated representation
more efficient for detecting MDD and PTSD. Furthermore, we modify the
hyper-parameters of the SSL encoder predicting fixed targets to generate global
representations that capture varying temporal contexts. Both these innovations
are noted to yield improved detection performances for considered mental
disorders and exhibit task-agnostic traits. In the context of the SSL model
predicting masked frames, the generated global representations are also noted
to exhibit task-agnostic traits.