A multi-task network predicts degradation patterns and a multiplicative Global Sensor Health Index from RGB images to provide early warnings of camera failure in autonomous driving before downstream detection degrades.
Benchmarking neural network ro- bustness to common corruptions and perturbations
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Zubov-Net aligns prescribed regions of attraction defined by learnable Lyapunov functions with true regions in Neural ODEs via a differentiable Zubov consistency loss, claiming to reconcile accuracy and certified robustness.
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Safety-Critical Camera Reliability Monitoring for ADAS via Degradation-Aware Uncertainty Pattern Analysis
A multi-task network predicts degradation patterns and a multiplicative Global Sensor Health Index from RGB images to provide early warnings of camera failure in autonomous driving before downstream detection degrades.
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Learning Aligned Stability in Neural ODEs Reconciling Accuracy with Robustness
Zubov-Net aligns prescribed regions of attraction defined by learnable Lyapunov functions with true regions in Neural ODEs via a differentiable Zubov consistency loss, claiming to reconcile accuracy and certified robustness.