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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 41–50 of 629 papers

TitleStatusHype
Reliability in Semantic Segmentation: Can We Use Synthetic Data?Code1
ID-like Prompt Learning for Few-Shot Out-of-Distribution DetectionCode1
Scaling for Training Time and Post-hoc Out-of-distribution Detection EnhancementCode1
Dream the Impossible: Outlier Imagination with Diffusion ModelsCode1
On the use of Mahalanobis distance for out-of-distribution detection with neural networks for medical imagingCode1
CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say NoCode1
How Good Are LLMs at Out-of-Distribution Detection?Code1
Unsupervised 3D out-of-distribution detection with latent diffusion modelsCode1
Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic AssemblyCode1
Beyond AUROC & co. for evaluating out-of-distribution detection performanceCode1
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