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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 291–300 of 629 papers

TitleStatusHype
Model-free Test Time Adaptation for Out-Of-Distribution Detection—0
EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering—0
EARLIN: Early Out-of-Distribution Detection for Resource-efficient Collaborative Inference—0
Dual Energy-Based Model with Open-World Uncertainty Estimation for Out-of-distribution Detection—0
Class-wise Thresholding for Robust Out-of-Distribution Detection—0
Dual Conditioned Diffusion Models for Out-Of-Distribution Detection: Application to Fetal Ultrasound Videos—0
Dual-Adapter: Training-free Dual Adaptation for Few-shot Out-of-Distribution Detection—0
Classifier-head Informed Feature Masking and Prototype-based Logit Smoothing for Out-of-Distribution Detection—0
A Simple Test-Time Method for Out-of-Distribution Detection—0
Double Descent Meets Out-of-Distribution Detection: Theoretical Insights and Empirical Analysis on the role of model complexity—0
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