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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 161–170 of 629 papers

TitleStatusHype
Detecting when pre-trained nnU-Net models fail silently for Covid-19 lung lesion segmentation—0
Boundary Aware Learning for Out-of-distribution Detection—0
Adversarial Distributions Against Out-of-Distribution Detectors—0
Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification—0
Few-Shot Graph Out-of-Distribution Detection with LLMs—0
An out-of-distribution discriminator based on Bayesian neural network epistemic uncertainty—0
FindMeIfYouCan: Bringing Open Set metrics to near , far and farther Out-of-Distribution Object Detection—0
Detecting Out-of-Distribution Examples with Gram Matrices—0
Boosting LLM-based Relevance Modeling with Distribution-Aware Robust Learning—0
Detecting Out-of-distribution Examples via Class-conditional Impressions Reappearing—0
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