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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 426–450 of 629 papers

TitleStatusHype
Out-of-Distribution Detection for LiDAR-based 3D Object Detection—0
Raising the Bar on the Evaluation of Out-of-Distribution Detection—0
Linking Neural Collapse and L2 Normalization with Improved Out-of-Distribution Detection in Deep Neural Networks—0
Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection—0
Distribution Calibration for Out-of-Domain Detection with Bayesian ApproximationCode0
Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification—0
Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training—0
Identifying Out-of-Distribution Samples in Real-Time for Safety-Critical 2D Object Detection with Margin Entropy Loss—0
Probing Contextual Diversity for Dense Out-of-Distribution DetectionCode0
Open-Set Semi-Supervised Object Detection—0
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
A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection using Compounded CorruptionsCode0
Task Agnostic and Post-hoc Unseen Distribution Detection—0
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
Sample-dependent Adaptive Temperature Scaling for Improved CalibrationCode0
Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors—0
A Baseline for Detecting Out-of-Distribution Examples in Image Captioning—0
Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring ApproachCode0
Harnessing Out-Of-Distribution Examples via Augmenting Content and StyleCode0
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