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Found 2 papers, 1 papers with code
Gradient-Regularized Out-of-Distribution Detection
In this work, we propose the idea of leveraging the information embedded in the gradient of the loss function during training to enable the network to not only learn a desired OOD score for each sample but also to exhibit similar behavior in a local neighborhood around each sample.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
ReachLipBnB: A branch-and-bound method for reachability analysis of neural autonomous systems using Lipschitz bounds
We propose a novel Branch-and-Bound method for reachability analysis of neural networks in both open-loop and closed-loop settings.
