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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 381–390 of 629 papers

TitleStatusHype
Out-of-Distribution Detection Using Peer-Class Generated by Large Language Model—0
Out-of-Distribution Detection with Reconstruction Error and Typicality-based Penalty—0
Out-of-Distribution Detection with Class Ratio Estimation—0
Out-of-Distribution Detection with Overlap Index—0
Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features—0
Out-of-distribution multi-view auto-encoders for prostate cancer lesion detection—0
Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder—0
Out-of-domain Detection based on Generative Adversarial Network—0
Overcoming Shortcut Problem in VLM for Robust Out-of-Distribution Detection—0
PnPOOD : Out-Of-Distribution Detection for Text Classification via Plug andPlay Data Augmentation—0
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