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Out of Distribution (OOD) Detection

Out of Distribution (OOD) Detection is the task of detecting instances that do not belong to the distribution the classifier has been trained on. OOD data is often referred to as "unseen" data, as the model has not encountered it during training.

OOD detection is typically performed by training a model to distinguish between in-distribution (ID) data, which the model has seen during training, and OOD data, which it has not seen. This can be done using a variety of techniques, such as training a separate OOD detector, or modifying the model's architecture or loss function to make it more sensitive to OOD data.

Papers

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Showing 531–540 of 629 papers

TitleStatusHype
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions—0
OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation—0
The Compact Support Neural Network—0
Joint Distribution across Representation Space for Out-of-Distribution Detection—0
Out-of-Distribution Detection of Melanoma using Normalizing Flows—0
SSD: A Unified Framework for Self-Supervised Outlier DetectionCode1
Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learning—0
A statistical framework for efficient out of distribution detection in deep neural networks—0
Bayesian OOD detection with aleatoric uncertainty and outlier exposure—0
Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural NetworksCode1
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