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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 281–290 of 629 papers

TitleStatusHype
Improving Confident-Classifiers For Out-of-distribution DetectionCode0
Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based LearningCode0
Improving Calibration and Out-of-Distribution Detection in Medical Image Segmentation with Convolutional Neural NetworksCode0
Improvements on Uncertainty Quantification for Node Classification via Distance-Based RegularizationCode0
Adversarial Self-Supervised Learning for Out-of-Domain DetectionCode0
Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain DetectionCode0
Large Class Separation is not what you need for Relational Reasoning-based OOD DetectionCode0
Approximations to the Fisher Information Metric of Deep Generative Models for Out-Of-Distribution DetectionCode0
ImageNet-OOD: Deciphering Modern Out-of-Distribution Detection AlgorithmsCode0
Layer Adaptive Deep Neural Networks for Out-of-distribution DetectionCode0
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