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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 191–200 of 629 papers

TitleStatusHype
A deep learning framework for the detection and quantification of drusen and reticular pseudodrusen on optical coherence tomography—0
Beyond Perceptual Distances: Rethinking Disparity Assessment for Out-of-Distribution Detection with Diffusion Models—0
Data Invariants to Understand Unsupervised Out-of-Distribution Detection—0
Federated Learning with Uncertainty via Distilled Predictive Distributions—0
GRODIN: Improved Large-Scale Out-of-Domain detection via Back-propagation—0
Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection—0
Curved Geometric Networks for Visual Anomaly Recognition—0
BAM: Box Abstraction Monitors for Real-time OoD Detection in Object Detection—0
Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification—0
COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification—0
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