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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 301–310 of 629 papers

TitleStatusHype
Block Selection Method for Using Feature Norm in Out-of-distribution DetectionCode1
YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution DetectionCode1
Improving Training and Inference of Face Recognition Models via Random Temperature Scaling—0
Rethinking Out-of-Distribution Detection From a Human-Centric Perspective—0
Out-Of-Distribution Detection Is Not All You Need—0
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic SegmentationCode1
TrustGAN: Training safe and trustworthy deep learning models through generative adversarial networksCode0
Beyond Mahalanobis-Based Scores for Textual OOD Detection—0
Multi-Level Knowledge Distillation for Out-of-Distribution Detection in TextCode1
Diffusion Denoising Process for Perceptron Bias in Out-of-distribution DetectionCode0
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