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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 11–20 of 629 papers

TitleStatusHype
Network Inversion for Uncertainty-Aware Out-of-Distribution Detection—0
Improving Out-of-Distribution Detection with Markov Logic Networks—0
Extremely Simple Multimodal Outlier Synthesis for Out-of-Distribution Detection and SegmentationCode2
GradPCA: Leveraging NTK Alignment for Reliable Out-of-Distribution Detection—0
SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps—0
Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selections and ApproximationsCode0
Humble your Overconfident Networks: Unlearning Overfitting via Sequential Monte Carlo Tempered Deep Ensembles—0
Unsupervised Out-of-Distribution Detection in Medical Imaging Using Multi-Exit Class Activation Maps and Feature MaskingCode0
Quantum Conflict Measurement in Decision Making for Out-of-Distribution Detection—0
Graph Synthetic Out-of-Distribution Exposure with Large Language Models—0
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