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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 401–410 of 629 papers

TitleStatusHype
Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection—0
OT-DETECTOR: Delving into Optimal Transport for Zero-shot Out-of-Distribution Detection—0
What do we learn? Debunking the Myth of Unsupervised Outlier Detection—0
Out-of-distribution detection algorithms for robust insect classification—0
Out-of-Distribution Detection and Data Drift Monitoring using Statistical Process Control—0
Out-of-Distribution Detection and Selective Generation for Conditional Language Models—0
Federated Learning with Uncertainty via Distilled Predictive Distributions—0
Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection—0
Out-of-distribution Partial Label Learning—0
Out of distribution detection for intra-operative functional imaging—0
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