RECL - Responsive Resource-Efficient Continuous Learning for Video Analytics

Explore a groundbreaking video analytics framework presented at NSDI '23 that revolutionizes continuous learning for real-time data processing. Dive into RECL (Responsive Resource-Efficient Continuous Learning), a system that seamlessly integrates model reuse and online retraining to swiftly adapt expert DNN models to specific video scenes. Discover how RECL overcomes the limitations of periodic retraining and historical model selection by implementing a shared "model zoo" across edge devices, employing rapid expert model selection, and dynamically optimizing GPU allocation. Learn about the framework's performance gains demonstrated through extensive evaluation on real-world videos for object detection and classification tasks. Gain insights into how RECL addresses the challenges of data drift and resource efficiency in video analytics, potentially transforming the field of continuous learning for large-scale applications.

RECL: Responsive Resource-Efficient Continuous Learning for Video Analytics