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Linga Reddy Cenkeramaddi

2 accepted papers

2024

Improving Unsupervised Domain Adaptation: A Pseudo-Candidate Set Approach

ECCV 2024poster

"Unsupervised domain adaptation (UDA) is a critical challenge in machine learning, aiming to transfer knowledge from a labeled source domain to an unlabeled target domain. In this work, we aim to improve target set accuracy in any existing UDA method by introducing an approach that utilizes pseudo-c…

Cited by 1SourcePDFScholar
2023

MADG: Margin-based Adversarial Learning for Domain Generalization

NeurIPS 2023poster

Domain Generalization (DG) techniques have emerged as a popular approach to address the challenges of domain shift in Deep Learning (DL), with the goal of generalizing well to the target domain unseen during the training. In recent years, numerous methods have been proposed to address the DG setting…

Cited by 34SourcePDFScholar