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08/30/2023
Robust Principles: Architectural Design Principles for Adversarially Robust CNNs
Our research aims to unify existing works' diverging opinions on how arc...
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01/08/2023
RobArch: Designing Robust Architectures against Adversarial Attacks
Adversarial Training is the most effective approach for improving the ro...
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07/21/2022
Synthetic Dataset Generation for Adversarial Machine Learning Research
Existing adversarial example research focuses on digitally inserted pert...
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05/13/2021
OpenFL: An open-source framework for Federated Learning
Federated learning (FL) is a computational paradigm that enables organiz...
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05/12/2021
The Federated Tumor Segmentation (FeTS) Challenge
This manuscript describes the first challenge on Federated Learning, nam...
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10/13/2020
Toward Few-step Adversarial Training from a Frequency Perspective
We investigate adversarial-sample generation methods from a frequency do...
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10/10/2018
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation
Deep learning models for semantic segmentation of images require large a...
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04/16/2018
