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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 331–340 of 629 papers

TitleStatusHype
Pseudo-OOD training for robust language models—0
Holistic Sentence Embeddings for Better Out-of-Distribution Detection—0
Exploiting Mixed Unlabeled Data for Detecting Samples of Seen and Unseen Out-of-Distribution Classes—0
OpenOOD: Benchmarking Generalized Out-of-Distribution DetectionCode0
How to Enable Uncertainty Estimation in Proximal Policy Optimization—0
Your Out-of-Distribution Detection Method is Not Robust!Code1
A Novel Explainable Out-of-Distribution Detection Approach for Spiking Neural NetworksCode0
Out-of-Distribution Detection and Selective Generation for Conditional Language Models—0
Out-of-Distribution Detection for LiDAR-based 3D Object Detection—0
Out-of-Distribution Detection with Hilbert-Schmidt Independence OptimizationCode1
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