Efficient visual learning and reasoning system with spurious and redundant data on edge computing
Type: dissertation
Abstract: The proliferation of deep visual learning has spurred the development of sophisticated applications deployed on resource-constrained edge devices, often operating in collaboration with the cloud.The practical efficiency and robustness of these edge-cloud visual systems, however, are fundamentally challenged by the inherent complexity of real-world data.This complexity manifests as performance-degrading artifacts at different stages of the machine learning lifecycle: during collaborative training, spurious data artifacts can corrupt the learning process, while during inference, informational redundancy in visual data can create debilitating latency.This thesis presents a unified approach to designing efficient and robust edge visual systems by systematically tackling these two critical data challenges.We make the following original contributions.
Author: Ling, Tao
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7217904140