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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 551–575 of 629 papers

TitleStatusHype
Improving Calibration and Out-of-Distribution Detection in Medical Image Segmentation with Convolutional Neural NetworksCode0
AUTO: Adaptive Outlier Optimization for Test-Time OOD DetectionCode0
Weak Distribution Detectors Lead to Stronger Generalizability of Vision-Language Prompt TuningCode0
Improvements on Uncertainty Quantification for Node Classification via Distance-Based RegularizationCode0
ImageNet-OOD: Deciphering Modern Out-of-Distribution Detection AlgorithmsCode0
OpenOOD: Benchmarking Generalized Out-of-Distribution DetectionCode0
Igeood: An Information Geometry Approach to Out-of-Distribution DetectionCode0
CVAD: A generic medical anomaly detector based on Cascade VAECode0
Uncertainty-Guided Appearance-Motion Association Network for Out-of-Distribution Action DetectionCode0
Open-World Lifelong Graph LearningCode0
XOOD: Extreme Value Based Out-Of-Distribution Detection For Image ClassificationCode0
Analysis of Confident-Classifiers for Out-of-distribution DetectionCode0
Outlier Synthesis via Hamiltonian Monte Carlo for Out-of-Distribution DetectionCode0
Structural Entropy Guided Unsupervised Graph Out-Of-Distribution DetectionCode0
Adversarial Self-Supervised Learning for Out-of-Domain DetectionCode0
A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution DetectionCode0
Out-of-distribution detection based on subspace projection of high-dimensional features output by the last convolutional layerCode0
Out-of-Distribution Detection based on In-Distribution Data Patterns Memorization with Modern Hopfield EnergyCode0
Out-of-Distribution Detection by Leveraging Between-Layer Transformation SmoothnessCode0
T2FNorm: Extremely Simple Scaled Train-time Feature Normalization for OOD DetectionCode0
TagOOD: A Novel Approach to Out-of-Distribution Detection via Vision-Language Representations and Class Center LearningCode0
Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion ImagesCode0
Out-of-Distribution Detection for Medical Applications: Guidelines for Practical EvaluationCode0
Contrastive Learning for OOD in Object detectionCode0
What If the Input is Expanded in OOD Detection?Code0
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