Sense and Learn: Self-Supervision for Omnipresent Sensors

TL;DR

This paper introduces Sense and Learn, a self-supervised framework for learning general-purpose representations from multisensor data, enabling effective transfer, semi-supervised, and few-shot learning without labeled data.

Contribution

It presents a generalized self-supervised learning framework for multisensor data that reduces reliance on labeled datasets and enhances transferability and few-shot learning capabilities.

Findings

Achieves competitive results with supervised methods on multiple datasets.

Significantly improves low-data regime performance with minimal labeled examples.

Learned representations are highly transferable across related datasets.

Abstract

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. It consists of several auxiliary tasks that can learn high-level and broadly useful features entirely from unannotated data without any human involvement in the tedious labeling process. We demonstrate the efficacy of our approach on several publicly available datasets from different domains and in various settings, including linear separability, semi-supervised or few shot learning, and transfer learning. Our methodology achieves results that are competitive with the supervised approaches and close the gap through fine-tuning a network while learning the downstream tasks in most cases. In particular, we show that the self-supervised network can be utilized as initialization to significantly boost the performance in a low-data regime with as few as 5 labeled instances per class, which is of high practical importance to real-world problems. Likewise, the learned representations with self-supervision are found to be highly transferable between related datasets, even when few labeled instances are available from the target domains. The self-learning nature of our methodology opens up exciting possibilities for on-device continual learning.