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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 131–140 of 629 papers

TitleStatusHype
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary CodesCode1
Learning Structured Representations with Hyperbolic EmbeddingsCode1
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution DataCode1
Learning with Mixture of Prototypes for Out-of-Distribution DetectionCode1
MASKER: Masked Keyword Regularization for Reliable Text ClassificationCode1
A Rate-Distortion View of Uncertainty QuantificationCode1
Dissecting Out-of-Distribution Detection and Open-Set Recognition: A Critical Analysis of Methods and BenchmarksCode1
CAN bus intrusion detection based on auxiliary classifier GAN and out-of-distribution detectionCode1
Isotropy Maximization Loss and Entropic Score: Accurate, Fast, Efficient, Scalable, and Turnkey Neural Networks Out-of-Distribution Detection Based on The Principle of Maximum EntropyCode1
Multidimensional Uncertainty-Aware Evidential Neural NetworksCode1
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