Non-adversarial Robustness of Deep Learning Methods for Computer Vision

Gojić, Gorana; Vincan, Vladimir; Kundacina, O; Miskovic, Dragisa; Dragan, Dinu

doi:10.1109/icetran59631.2023.10192125

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Book1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Overview of Deep Learning Methods for Retinal Vessel Segmentation

Gojić,

Kundačina,

Mišković

et al. 2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Overview of Deep Learning Methods for Retinal Vessel Segmentation

Gojić,

Kundačina,

Mišković

et al. 2024

Lecture Notes in Networks and Systems

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

Non-adversarial Robustness of Deep Learning Methods for Computer Vision

Cited by 2 publications

References 27 publications

Overview of Deep Learning Methods for Retinal Vessel Segmentation

Overview of Deep Learning Methods for Retinal Vessel Segmentation

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

Contact Info

Product

Resources

About