Search Results for author:
Found 5 papers, 2 papers with code
FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical Imaging
In response, we introduce FedMedICL, a unified framework and benchmark to holistically evaluate federated medical imaging challenges, simultaneously capturing label, demographic, and temporal distribution shifts.
Real-Time Evaluation in Online Continual Learning: A New Hope
We show that a simple baseline outperforms state-of-the-art CL methods under this evaluation, questioning the applicability of existing methods in realistic settings.
PIVOT: Prompting for Video Continual Learning
In this paper, we address the problem of continual learning for video data.
Generalizability of Adversarial Robustness Under Distribution Shifts
Recent progress in empirical and certified robustness promises to deliver reliable and deployable Deep Neural Networks (DNNs).
vCLIMB: A Novel Video Class Incremental Learning Benchmark
We perform in-depth evaluations of existing CL methods in vCLIMB, and observe two unique challenges in video data.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
