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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 441–450 of 629 papers

TitleStatusHype
Trustworthy Long-Tailed ClassificationCode1
Unsupervised Approaches for Out-Of-Distribution Dermoscopic Lesion Detection—0
Out of distribution detection for skin and malaria images—0
kFolden: k-Fold Ensemble for Out-Of-Distribution Detection—0
Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing—0
PnPOOD : Out-Of-Distribution Detection for Text Classification via Plug andPlay Data Augmentation—0
CVAD: A generic medical anomaly detector based on Cascade VAECode0
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL—0
Class-wise Thresholding for Robust Out-of-Distribution Detection—0
Exploring Covariate and Concept Shift for Detection and Calibration of Out-of-Distribution Data—0
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