Continual Source-Free Unsupervised Domain Adaptation

Ahmed, Waqar; Morerio, Pietro; Murino, Vittorio

doi:10.1007/978-3-031-43148-7_2

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article2

Other1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

RobustDA: Lightweight Robust Domain Adaptation for Evolving Data at Edge

Guo,

Zuo,

Han

et al. 2024

IEEE J. Emerg. Sel. Topics Circuits Syst.

View full text Add to dashboard Cite

RobustDA: Lightweight Robust Domain Adaptation for Evolving Data at Edge

Guo,

Zuo,

Han

et al. 2024

IEEE J. Emerg. Sel. Topics Circuits Syst.

View full text Add to dashboard Cite

Domain-Aware Knowledge Distillation for Continual Model Generalization

Reddy,

Baktashmotlagh,

Arora

2024

2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

View full text Add to dashboard Cite

An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision

Tanveer,

Fatima,

Zardari

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

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 opportunities and challenges associated with this topic. Domain adaptation methods play a pivotal role in facilitating seamless knowledge transfer and enhancing the generalization capabilities of computer and robotic vision systems. Our methodology involves systematic data collection and preparation, followed by the application of diverse assessment metrics to evaluate the efficacy of domain adaptation strategies. This study assesses the effectiveness and versatility of conventional, deep learning-based, and hybrid domain adaptation techniques within the domains of computer and robotic vision. Through a cross-domain analysis, we scrutinize the performance of these approaches in different contexts, shedding light on their strengths and limitations. The findings gleaned from our evaluation of specific domains and models offer valuable insights for practical applications while reinforcing the validity of the proposed methodologies.

show abstract

Continual Source-Free Unsupervised Domain Adaptation

Cited by 3 publications

References 25 publications

RobustDA: Lightweight Robust Domain Adaptation for Evolving Data at Edge

RobustDA: Lightweight Robust Domain Adaptation for Evolving Data at Edge

Domain-Aware Knowledge Distillation for Continual Model Generalization

An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision

Contact Info

Product

Resources

About