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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 341–350 of 629 papers

TitleStatusHype
Raising the Bar on the Evaluation of Out-of-Distribution Detection—0
Linking Neural Collapse and L2 Normalization with Improved Out-of-Distribution Detection in Deep Neural Networks—0
Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection—0
Distribution Calibration for Out-of-Domain Detection with Bayesian ApproximationCode0
Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification—0
CAN bus intrusion detection based on auxiliary classifier GAN and out-of-distribution detectionCode1
Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training—0
Identifying Out-of-Distribution Samples in Real-Time for Safety-Critical 2D Object Detection with Margin Entropy Loss—0
Probing Contextual Diversity for Dense Out-of-Distribution DetectionCode0
Open-Set Semi-Supervised Object Detection—0
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