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Estimating the Robustness Radius for Randomized Smoothing with 100$\times$ Sample Efficiency
Randomized smoothing (RS) has successfully been used to improve the robustness of predictions for deep neural networks (DNNs) by adding random noise to create multiple variations of an input, followed by deciding the consensus.
Prioritizing Corners in OoD Detectors via Symbolic String Manipulation
We consider the definition of "in-distribution" characterized in the feature space by a union of hyperrectangles learned from the training dataset.
Unaligned but Safe -- Formally Compensating Performance Limitations for Imprecise 2D Object Detection
We address a special sub-type of performance limitations: the prediction bounding box cannot be perfectly aligned with the ground truth, but the computed Intersection-over-Union metric is always larger than a given threshold.
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