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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 351–360 of 629 papers

TitleStatusHype
NODI: Out-Of-Distribution Detection with Noise from Diffusion—0
NoiER: An Approach for Training more Reliable Fine-TunedDownstream Task Models—0
'No' Matters: Out-of-Distribution Detection in Multimodality Long Dialogue—0
No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection—0
No Shifted Augmentations (NSA): strong baselines for self-supervised Anomaly Detection—0
Novelty Detection Via Blurring—0
On the Learnability of Out-of-distribution Detection—0
OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples—0
OOD Aware Supervised Contrastive Learning—0
OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation—0
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