Coward detects backdoors in federated learning by injecting a collision-suppressed watermark on OOD data to invert the detection paradigm and limit OOD bias effects.
On calibration of modern neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Fusing perceptual and dynamics anomaly scores enables online temperature scaling that cuts expected calibration error by 37% on physical DonkeyCar tests with four unseen anomaly types.
citing papers explorer
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Coward: Collision-based OOD Watermarking for Practical Proactive Federated Backdoor Detection
Coward detects backdoors in federated learning by injecting a collision-suppressed watermark on OOD data to invert the detection paradigm and limit OOD bias effects.
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Anomaly-Informed Confidence Calibration for Vision-Based Safety Prediction
Fusing perceptual and dynamics anomaly scores enables online temperature scaling that cuts expected calibration error by 37% on physical DonkeyCar tests with four unseen anomaly types.