A comprehensive survey of forgetting in deep learning beyond continual learning
Forgetting refers to the loss or deterioration of previously acquired knowledge. While
existing surveys on forgetting have primarily focused on continual learning, forgetting is a …
existing surveys on forgetting have primarily focused on continual learning, forgetting is a …
An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
This review article comprehensively delves into the rapidly evolving field of domain
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …
Towards Domain-Aware Knowledge Distillation for Continual Model Generalization
N Reddy, M Baktashmotlagh… - Proceedings of the IEEE …, 2024 - openaccess.thecvf.com
Generalization on unseen domains is critical for Deep Neural Networks (DNNs) to perform
well in real-world applications such as autonomous navigation. However, catastrophic …
well in real-world applications such as autonomous navigation. However, catastrophic …
RobustDA: Lightweight Robust Domain Adaptation for Evolving Data at Edge
AI applications powered by deep learning models are increasingly run natively at edge. A
deployed model not only encounters continuously evolving input distributions (domains) but …
deployed model not only encounters continuously evolving input distributions (domains) but …
Domain-Aware Knowledge Distillation for Continual Model Generalization
N Reddy, M Baktashmotlagh… - 2024 IEEE/CVF Winter …, 2024 - ieeexplore.ieee.org
Generalization on unseen domains is critical for Deep Neural Networks (DNNs) to perform
well in real-world applications such as autonomous navigation. However, catastrophic …
well in real-world applications such as autonomous navigation. However, catastrophic …