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Found 4 papers, 2 papers with code
UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs
We present an approach to quantifying both aleatoric and epistemic uncertainty for deep neural networks in image classification, based on generative adversarial networks (GANs).
Out of Distribution (OOD) Detection
Uncertainty Quantification
Detection and Retrieval of Out-of-Distribution Objects in Semantic Segmentation
When deploying deep learning technology in self-driving cars, deep neural networks are constantly exposed to domain shifts.
Exploring Confidence Measures for Word Spotting in Heterogeneous Datasets
In recent years, convolutional neural networks (CNNs) took over the field of document analysis and they became the predominant model for word spotting.
Classification Uncertainty of Deep Neural Networks Based on Gradient Information
If we however allow the meta classifier to be trained on uncertainty metrics for some out-of-distribution samples, meta classification for concepts remote from EMNIST digits (then termed known unknowns) can be improved considerably.
