Search Results for author:
Found 6 papers, 3 papers with code
Automated Knowledge Modeling for Cancer Clinical Practice Guidelines
Clinical Practice Guidelines (CPGs) for cancer diseases evolve rapidly due to new evidence generated by active research.
TAME: Task Agnostic Continual Learning using Multiple Experts
Our algorithm, which we call TAME (Task-Agnostic continual learning using Multiple Experts), automatically detects the shift in data distributions and switches between task expert networks in an online manner.
A Closer Look at Smoothness in Domain Adversarial Training
Based on the analysis, we introduce the Smooth Domain Adversarial Training (SDAT) procedure, which effectively enhances the performance of existing domain adversarial methods for both classification and object detection tasks.
Ranked #5 on
Domain Adaptation
on VisDA2017
S$^3$VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation
Unsupervised domain adaptation (DA) methods have focused on achieving maximal performance through aligning features from source and target domains without using labeled data in the target domain.
S3VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation
Unsupervised domain adaptation (DA) methods have focused on achieving maximal performance through aligning features from source and target domains without using labeled data in the target domain.
A Novel Hybrid CNN-AIS Visual Pattern Recognition Engine
In this paper we propose to apply CNN to small data sets like for example, personal albums or other similar environs where the size of training dataset is a limitation, within the framework of a proposed hybrid CNN-AIS model.
