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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 531–540 of 629 papers

TitleStatusHype
Back to the Basics: Revisiting Out-of-Distribution Detection BaselinesCode0
Detecting Out-of-distribution Data through In-distribution Class PriorCode0
No True State-of-the-Art? OOD Detection Methods are Inconsistent across DatasetsCode0
WAIC, but Why? Generative Ensembles for Robust Anomaly DetectionCode0
Enhancing OOD Detection Using Latent DiffusionCode0
Kernel PCA for Out-of-Distribution DetectionCode0
On Out-of-Distribution Detection for Audio with Deep Nearest NeighborsCode0
ITP: Instance-Aware Test Pruning for Out-of-Distribution DetectionCode0
On the detection of Out-Of-Distribution samples in Multiple Instance LearningCode0
Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain DetectionCode0
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