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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 451–460 of 629 papers

TitleStatusHype
Understanding Softmax Confidence and Uncertainty—0
Understanding the properties and limitations of contrastive learning for Out-of-Distribution detection—0
Understanding the Role of Self-Supervised Learning in Out-of-Distribution Detection Task—0
Unified Out-Of-Distribution Detection: A Model-Specific Perspective—0
Unsupervised Approaches for Out-Of-Distribution Dermoscopic Lesion Detection—0
Unsupervised Evaluation of Out-of-distribution Detection: A Data-centric Perspective—0
Unsupervised Layer-wise Score Aggregation for Textual OOD Detection—0
Unsupervised Out-of-Distribution Detection with Batch Normalization—0
Using Semantic Information for Defining and Detecting OOD Inputs—0
VisTa: Visual-contextual and Text-augmented Zero-shot Object-level OOD Detection—0
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