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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 371–380 of 629 papers

TitleStatusHype
Out of distribution detection for skin and malaria images—0
Out of Distribution Detection, Generalization, and Robustness Triangle with Maximum Probability Theorem—0
Out-of-Distribution Detection in Dermatology using Input Perturbation and Subset Scanning—0
Out-of-distribution Detection in Medical Image Analysis: A survey—0
Out-of-Distribution Detection in Multi-Label Datasets using Latent Space of β-VAE—0
Out-Of-Distribution Detection In Unsupervised Continual Learning—0
Out-Of-Distribution Detection Is Not All You Need—0
Out-of-Distribution Detection of Melanoma using Normalizing Flows—0
Out-of-Distribution Detection Should Use Conformal Prediction (and Vice-versa?)—0
Out-of-Distribution Detection Using Neural Rendering Generative Models—0
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