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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 321–330 of 629 papers

TitleStatusHype
Efficient Out-of-Distribution Detection via CVAE data Generation—0
Efficient Out-of-Distribution Detection Using Latent Space of β-VAE for Cyber-Physical Systems—0
Towards Few-shot Out-of-Distribution Detection—0
A Closer Look at the Learnability of Out-of-Distribution (OOD) Detection—0
MADOD: Generalizing OOD Detection to Unseen Domains via G-Invariance Meta-Learning—0
Efficient Out-of-Distribution Detection in Digital Pathology Using Multi-Head Convolutional Neural Networks—0
MCROOD: Multi-Class Radar Out-Of-Distribution Detection—0
Efficacy of Pixel-Level OOD Detection for Semantic Segmentation—0
Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Space—0
Meta Learning Low Rank Covariance Factors for Energy Based Deterministic Uncertainty—0
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