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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 461–470 of 629 papers

TitleStatusHype
Out-of-Distribution Detection for Medical Applications: Guidelines for Practical EvaluationCode0
Intra-class Mixup for Out-of-Distribution Detection—0
MOG: Molecular Out-of-distribution Generation with Energy-based Models—0
FROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection—0
Revisiting flow generative models for Out-of-distribution detection—0
Exploring Covariate and Concept Shift for Detection and Confidence Calibration of Out-of-Distribution Data—0
GRODIN: Improved Large-Scale Out-of-Domain detection via Back-propagation—0
Decomposing Texture and Semantics for Out-of-distribution Detection—0
Revisiting Out-of-Distribution Detection: A Simple Baseline is Surprisingly Effective—0
Efficient Out-of-Distribution Detection via CVAE data Generation—0
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