Search Results for author:
Found 7 papers, 4 papers with code
Online Continual Domain Adaptation for Semantic Image Segmentation Using Internal Representations
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 to maintain performance.
Preserving Fairness in AI under Domain Shift
Our algorithm is based on updating the model such that the internal representation of data remains unbiased despite distributional shifts in the input space.
Unsupervised Model Adaptation for Source-free Segmentation of Medical Images
We rely on an approximation of the source latent features at adaptation time, and create a joint source/target embedding space by minimizing a distributional distance metric based on optimal transport.
Secure Domain Adaptation with Multiple Sources
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 annotated source domains.
Multi-Source Unsupervised Domain Adaptation
Unsupervised Domain Adaptation
Domain Adaptation for the Segmentation of Confidential Medical Images
In this work, we develop an algorithm for UDA where the source domain data is inaccessible during target adaptation.
Unsupervised Model Adaptation for Continual Semantic Segmentation
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.
Probabilistic Submodular Maximization in Sub-Linear Time
As a remedy, we introduce the problem of sublinear time probabilistic submodular maximization: Given training examples of functions (e. g., via user feature vectors), we seek to reduce the ground set so that optimizing new functions drawn from the same distribution will provide almost as much value when restricted to the reduced ground set as when using the full set.
