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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
Beyond Mahalanobis-Based Scores for Textual OOD Detection—0
Diffusion Denoising Process for Perceptron Bias in Out-of-distribution DetectionCode0
Towards Realistic Out-of-Distribution Detection: A Novel Evaluation Framework for Improving Generalization in OOD DetectionCode0
A Benchmark for Out of Distribution Detection in Point Cloud 3D Semantic Segmentation—0
Estimating Soft Labels for Out-of-Domain Intent Detection—0
Interpreting deep learning output for out-of-distribution detection—0
Understanding the properties and limitations of contrastive learning for Out-of-Distribution detection—0
Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation—0
On Out-of-Distribution Detection for Audio with Deep Nearest NeighborsCode0
Is Out-of-Distribution Detection Learnable?—0
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