Don't freeze: Finetune encoders for better Self-Supervised HAR
Vitor Fortes Rey Dominique Nshimyimana Paul Lukowicz
Recently self-supervised learning has been proposed in the field of human
activity recognition as a solution to the labelled data availability problem.
The idea being that by using pretext tasks such as reconstruction or
contrastive predictive coding, useful representations can be learned that then
can be used for classification. Those approaches follow the pretrain, freeze
and fine-tune procedure. In this paper we will show how a simple change - not
freezing the representation - leads to substantial performance gains across
pretext tasks. The improvement was found in all four investigated datasets and
across all four pretext tasks and is inversely proportional to amount of
labelled data. Moreover the effect is present whether the pretext task is
carried on the Capture24 dataset or directly in unlabelled data of the target
dataset.