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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 351–400 of 629 papers

TitleStatusHype
SAFE: Sensitivity-Aware Features for Out-of-Distribution Object DetectionCode1
A Multi-Head Model for Continual Learning via Out-of-Distribution ReplayCode1
Out-of-distribution Detection via Frequency-regularized Generative ModelsCode1
Contrastive Learning for OOD in Object detectionCode0
Efficient Out-of-Distribution Detection of Melanoma with Wavelet-based Normalizing FlowsCode0
Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation—0
Curved Geometric Networks for Visual Anomaly Recognition—0
XOOD: Extreme Value Based Out-Of-Distribution Detection For Image ClassificationCode0
Out-of-Distribution Detection with Semantic Mismatch under MaskingCode1
A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection using Compounded CorruptionsCode0
Task Agnostic and Post-hoc Unseen Distribution Detection—0
CODiT: Conformal Out-of-Distribution Detection in Time-Series DataCode1
Tailoring Self-Supervision for Supervised LearningCode1
Instance-Aware Observer Network for Out-of-Distribution Object Segmentation—0
A Simple Test-Time Method for Out-of-Distribution Detection—0
On the Usefulness of Deep Ensemble Diversity for Out-of-Distribution DetectionCode0
Augmenting Softmax Information for Selective Classification with Out-of-Distribution DataCode1
Sample-dependent Adaptive Temperature Scaling for Improved CalibrationCode0
A Baseline for Detecting Out-of-Distribution Examples in Image Captioning—0
Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors—0
Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring ApproachCode0
Out of Distribution Detection via Neural Network AnchoringCode1
Harnessing Out-Of-Distribution Examples via Augmenting Content and StyleCode0
Back to the Basics: Revisiting Out-of-Distribution Detection BaselinesCode0
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed RecognitionCode1
Revisit Overconfidence for OOD Detection: Reassigned Contrastive Learning with Adaptive Class-dependent ThresholdCode0
Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion ImagesCode0
EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering—0
SHELS: Exclusive Feature Sets for Novelty Detection and Continual Learning Without Class BoundariesCode0
POEM: Out-of-Distribution Detection with Posterior SamplingCode1
Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection—0
WeShort: Out-of-distribution Detection With Weak Shortcut structure—0
Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective—0
Multiple Testing Framework for Out-of-Distribution Detection—0
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core QuantitiesCode1
Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Space—0
Supervision Adaptation Balancing In-distribution Generalization and Out-of-distribution Detection—0
READ: Aggregating Reconstruction Error into Out-of-distribution Detection—0
Morphence-2.0: Evasion-Resilient Moving Target Defense Powered by Out-of-Distribution DetectionCode1
Federated Learning with Uncertainty via Distilled Predictive Distributions—0
Out-of-Distribution Detection with Class Ratio Estimation—0
What do we learn? Debunking the Myth of Unsupervised Outlier Detection—0
Accuracy on In-Domain Samples Matters When Building Out-of-Domain detectors: A Reply to Marek et al. (2021)Code1
Transformer-based out-of-distribution detection for clinically safe segmentationCode0
How Useful are Gradients for OOD Detection Really?—0
Robust Representation via Dynamic Feature AggregationCode0
KNN-Contrastive Learning for Out-of-Domain Intent Classification—0
Evaluating the Practical Utility of Confidence-score based Techniques for Unsupervised Open-world Classification—0
Learning by Erasing: Conditional Entropy based Transferable Out-Of-Distribution DetectionCode0
Metric Learning and Adaptive Boundary for Out-of-Domain DetectionCode0
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