Description of field of research:
In the context of mobile sensing environments, various sensors on mobile devices continually generate a vast amount of data. Analyzing this ever-increasing data presents several challenges, including limited access to annotated data and a constantly changing environment. Recent advancements in self-supervised learning have been utilized as a pre-training step to enhance the performance of conventional supervised models to address the absence of labelled datasets. This research examines the impact of using a self-supervised representation learning model for time series classification tasks in which data is incrementally available.
School
Research Area
Deep learning | Time-series | Self-supervised learning
- Research Environment
- Expected Outcomes
- Reference Material/Links
- Our team
The experiments and the framework will be deployed using Python (Pytorch | Tensorflow)
- Continual learning framework for wearable sensors (expansion on our existing works)
- 1 or 2 papers on incremental learning with self-supervised pretraining , and self-supervised continual learning (SSCL) on wearable sensor data
