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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 571–580 of 629 papers

TitleStatusHype
Informative Outlier Matters: Robustifying Out-of-distribution Detection Using Outlier Mining—0
An Algorithm for Out-Of-Distribution Attack to Neural Network EncoderCode0
FOOD: Fast Out-Of-Distribution DetectorCode1
Certifiably Adversarially Robust Detection of Out-of-Distribution DataCode1
Contrastive Training for Improved Out-of-Distribution Detection—0
Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks—0
A Critical Evaluation of Open-World Machine Learning—0
Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks—0
ATOM: Robustifying Out-of-distribution Detection Using Outlier MiningCode1
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?Code1
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