English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Deep neural networks (DNNs) are known to produce incorrect predictions withvery high confidence on out-of-distribution (OOD) inputs. This limitation isone of the key challenges in the adoption of deep learning models inhigh-assurance systems such as autonomous driving, air traffic management, andmedical diagnosis. This challenge has received significant attention recently,and several techniques have been developed to detect inputs where the model'sprediction cannot be trusted. These techniques use different statistical,geometric, or topological signatures. This paper presents a taxonomy of OODoutlier inputs based on their source and nature of uncertainty. We demonstratehow different existing detection approaches fail to detect certain types ofoutliers. We utilize these insights to develop a novel integrated detectionapproach that uses multiple attributes corresponding to different types ofoutliers. Our results include experiments on CIFAR10, SVHN and MNIST asin-distribution data and Imagenet, LSUN, SVHN (for CIFAR10), CIFAR10 (forSVHN), KMNIST, and F-MNIST as OOD data across different DNN architectures suchas ResNet34, WideResNet, DenseNet, and LeNet5.

Are all outliers alike? On Understanding the Diversity of Outliers for Detecting OODs