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Found 6 papers, 4 papers with code
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.
Out-of-distribution Detection in Classifiers via Generation
By design, discriminatively trained neural network classifiers produce reliable predictions only for in-distribution samples.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Improving Confident-Classifiers For Out-of-distribution Detection
In the context of OOD detection for image classification, one of the recent approaches proposes training a classifier called “confident-classifier” by minimizing the standard cross-entropy loss on in-distribution samples and minimizing the KLdivergence between the predictive distribution of OOD samples in the low-density“boundary” of in-distribution and the uniform distribution (maximizing the entropy of the outputs).
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
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.
