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Found 7 papers, 3 papers with code
Adversarial Imaging Pipelines
As a result, optimized patterns can become adversarial for the classifier after being transformed by a certain camera ISP and optic but not for others.
Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar
In this work, we depart from visible-wavelength approaches and demonstrate detection, classification, and tracking of hidden objects in large-scale dynamic environments using Doppler radars that can be manufactured at low-cost in series production.
Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output
Several approaches have been proposed to detect OOD inputs, but the detection task is still an ongoing challenge.
Analysis of Confident-Classifiers for Out-of-distribution Detection
Discriminatively trained neural classifiers can be trusted, only when the input data comes from the training distribution (in-distribution).
Improving Reconstruction Autoencoder Out-of-distribution Detection with Mahalanobis Distance
There is an increasingly apparent need for validating the classifications made by deep learning systems in safety-critical applications like autonomous vehicle systems.
Calibrating Uncertainties in Object Localization Task
In many safety-critical applications such as autonomous driving and surgical robots, it is desirable to obtain prediction uncertainties from object detection modules to help support safe decision-making.
Improve Uncertainty Estimation for Unknown Classes in Bayesian Neural Networks with Semi-Supervised /One Set Classification
Although deep neural network (DNN) has achieved many state-of-the-art results, estimating the uncertainty presented in the DNN model and the data is a challenging task.
