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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 431–440 of 629 papers

TitleStatusHype
WOOD: Wasserstein-based Out-of-Distribution DetectionCode1
Hyperdimensional Feature Fusion for Out-Of-Distribution DetectionCode1
Benchmark for Out-of-Distribution Detection in Deep Reinforcement Learning—0
Provable Guarantees for Understanding Out-of-distribution DetectionCode1
Decomposing Representations for Deterministic Uncertainty Estimation—0
STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data—0
Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models—0
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection—0
Data Invariants to Understand Unsupervised Out-of-Distribution Detection—0
DICE: Leveraging Sparsification for Out-of-Distribution DetectionCode1
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