5 citations · 14 across the 5 of their papers we have counts for
6 papers
Online Continual Domain Adaptation for Semantic Image Segmentation Using Internal Representations
Serban Stan, Mohammad Rostami
Semantic segmentation models trained on annotated data fail to generalize well when the input data distribution changes over extended time period, leading to requiring re-training…
Preserving Fairness in AI under Domain Shift
Serban Stan, Mohammad Rostami
Existing algorithms for ensuring fairness in AI use a single-shot training strategy, where an AI model is trained on an annotated training dataset with sensitive attributes and the…
Unsupervised Model Adaptation for Source-free Segmentation of Medical Images
Serban Stan, Mohammad Rostami
The recent prevalence of deep neural networks has lead semantic segmentation networks to achieve human-level performance in the medical field when sufficient training data is provi…
Secure Domain Adaptation with Multiple Sources
Serban Stan, Mohammad Rostami
Multi-source unsupervised domain adaptation (MUDA) is a framework to address the challenge of annotated data scarcity in a target domain via transferring knowledge from multiple an…
Domain Adaptation for the Segmentation of Confidential Medical Images
Serban Stan, Mohammad Rostami
Convolutional neural networks (CNNs) have led to significant improvements in the semantic segmentation of images. When source and target datasets come from different modalities, CN…
Unsupervised Model Adaptation for Continual Semantic Segmentation
Serban Stan, Mohammad Rostami
We develop an algorithm for adapting a semantic segmentation model that is trained using a labeled source domain to generalize well in an unlabeled target domain. A similar problem…