The Machine learning model has two problems, they are Overfitting and Under-fitting.
Underfitting is a statistical model or a machine learning algorithm, it cannot capture the …

We study the problem of robust domain adaptation in the context of unavailable target labels
and source data. The considered robustness is against adversarial perturbations. This paper …

Unsupervised Domain Adaptation (UDA) for semantic segmentation has received
widespread attention for its ability to transfer knowledge from the source to target domains …

Distributional uncertainty exists broadly in many real-world applications, one of which in the
form of domain discrepancy. Yet in the existing literature, the mathematical definition of it is …

Modern detectors are mostly trained under single and limited conditions. However, object
detection faces various complex and open situations in autonomous driving, especially in …

Artificial Intelligence based medical diagnosis is a new approach in medical field, wherein
pathologists are no need to work with glass, but they are using pixels to identify the disease …

At present, the prevalence of diabetes is increasing because the human body cannot
metabolize the glucose level. Accurate prediction of diabetes patients is an important …

In this paper we test and ultimately confirm the texture-bias hypothesis of the state of the art
method for semantic segmentation, DeepLabv3+. However, our results show that even …

Autonomous systems strongly rely on computer vision to build a comprehensive model for
understanding the environment they are embedded in. This task needs to be solved on …

Abstract Domain Adaptation (DA) studies how to improve model performance on a target
domain, with additional training data from one or more source domain (s) that are under …