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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 471–480 of 629 papers

TitleStatusHype
Data Invariants to Understand Unsupervised Out-of-Distribution Detection—0
Reconstruction-based Out-of-Distribution Detection for Short-Range FMCW Radar—0
Understanding the properties and limitations of contrastive learning for Out-of-Distribution detection—0
Curved Geometric Networks for Visual Anomaly Recognition—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
Understanding the Role of Self-Supervised Learning in Out-of-Distribution Detection Task—0
Rethinking Out-of-Distribution Detection From a Human-Centric Perspective—0
Controlling Neural Collapse Enhances Out-of-Distribution Detection and Transfer Learning—0
Bayesian OOD detection with aleatoric uncertainty and outlier exposure—0
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