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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 371–380 of 629 papers

TitleStatusHype
No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection—0
No Shifted Augmentations (NSA): strong baselines for self-supervised Anomaly Detection—0
Towards Rigorous Design of OoD Detectors—0
Novelty Detection Via Blurring—0
Towards Textual Out-of-Domain Detection without In-Domain Labels—0
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions—0
Towards Unknown-aware Deep Q-Learning—0
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization—0
Towards Unknown-aware Learning with Virtual Outlier Synthesis—0
Distributionally Robust Recurrent Decoders with Random Network Distillation—0
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