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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 521–530 of 629 papers

TitleStatusHype
Few-Shot Graph Out-of-Distribution Detection with LLMs—0
FindMeIfYouCan: Bringing Open Set metrics to near , far and farther Out-of-Distribution Object Detection—0
Falsehoods that ML researchers believe about OOD detection—0
Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification—0
Extremely Simple Out-of-distribution Detection for Audio-visual Generalized Zero-shot Learning—0
FOOD: Facial Authentication and Out-of-Distribution Detection with Short-Range FMCW Radar—0
Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learning—0
Free Lunch for Generating Effective Outlier Supervision—0
FROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection—0
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection—0
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