Sense and Learn: Self-Supervision for Omnipresent Sensors

Understand

Learning general-purpose representations from multisensor data produced by the omnipresent sensing systems (or IoT in general) has numerous applications in diverse use cases.

• Existing purely supervised end-to-end deep learning techniques depend on the availability of a massive amount of well-curated data, acquiring which is notoriously difficult but required to achieve a sufficient level of generalization on a task of interest.
• In this work, we leverage the self-supervised learning paradigm towards realizing the vision of continual learning from unlabeled inputs.
• We present a generalized framework named Sense and Learn for representation or feature learning from raw sensory data.