Learning Compact Representation with Less Labeled Data from Sensors

Learning Compact Representation with Less Labeled Data from Sensors

Explore data-efficient learning techniques for compact representations in sensor-based human behavior modeling. Delve into the challenges of limited labeled data in IoT environments and discover innovative approaches like domain adaptation and pretraining. Learn about contrastive learning, change point detection, and their applications in behavior recognition and COVID-19 cough sound detection. Examine evaluation datasets and experiment results to gain insights into scalable contrastive networks and their performance in real-world scenarios.