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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 251–275 of 629 papers

TitleStatusHype
Non-Linear Outlier Synthesis for Out-of-Distribution DetectionCode0
Enhancing OOD Detection Using Latent DiffusionCode0
On the detection of Out-Of-Distribution samples in Multiple Instance LearningCode0
Hybrid Energy Based Model in the Feature Space for Out-of-Distribution DetectionCode0
Efficient Out-of-Distribution Detection of Melanoma with Wavelet-based Normalizing FlowsCode0
A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution DetectionCode0
NCDD: Nearest Centroid Distance Deficit for Out-Of-Distribution Detection in Gastrointestinal VisionCode0
Multi-Label Out-of-Distribution Detection with Spectral Normalized Joint EnergyCode0
Out-of-Distribution Detection based on In-Distribution Data Patterns Memorization with Modern Hopfield EnergyCode0
Mining In-distribution Attributes in Outliers for Out-of-distribution DetectionCode0
Metric Learning and Adaptive Boundary for Out-of-Domain DetectionCode0
Long-Tailed Out-of-Distribution Detection: Prioritizing Attention to TailCode0
Long-Tailed Out-of-Distribution Detection via Normalized Outlier Distribution AdaptationCode0
Towards Realistic Out-of-Distribution Detection: A Novel Evaluation Framework for Improving Generalization in OOD DetectionCode0
Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection In Task Oriented DialogCode0
Do Bayesian Variational Autoencoders Know What They Don't Know?Code0
LEGO-Learn: Label-Efficient Graph Open-Set LearningCode0
Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and AccuracyCode0
Can Pre-trained Networks Detect Familiar Out-of-Distribution Data?Code0
Leveraging Perturbation Robustness to Enhance Out-of-Distribution DetectionCode0
Distribution Calibration for Out-of-Domain Detection with Bayesian ApproximationCode0
Are Bayesian neural networks intrinsically good at out-of-distribution detection?Code0
Learning by Erasing: Conditional Entropy based Transferable Out-Of-Distribution DetectionCode0
Large Class Separation is not what you need for Relational Reasoning-based OOD DetectionCode0
Layer Adaptive Deep Neural Networks for Out-of-distribution DetectionCode0
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