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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 351–375 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
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