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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 571–580 of 629 papers

TitleStatusHype
Identifying Out-of-Distribution Samples in Real-Time for Safety-Critical 2D Object Detection with Margin Entropy Loss—0
EPA: Neural Collapse Inspired Robust Out-of-Distribution Detector—0
Using Semantic Information for Defining and Detecting OOD Inputs—0
Supervision Adaptation Balancing In-distribution Generalization and Out-of-distribution Detection—0
Out-of-distribution detection for regression tasks: parameter versus predictor entropy—0
Entropic Issues in Likelihood-Based OOD Detection—0
PAWS-VMK: A Unified Approach To Semi-Supervised Learning And Out-of-Distribution Detection—0
SupEuclid: Extremely Simple, High Quality OoD Detection with Supervised Contrastive Learning and Euclidean Distance—0
A Metacognitive Approach to Out-of-Distribution Detection for Segmentation—0
A Critical Evaluation of Open-World Machine Learning—0
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