Out-of-Distribution Detection
170 papers with code • 40 benchmarks • 14 datasets
Detect out-of-distribution or anomalous examples.
Benchmarks
These leaderboards are used to track progress in Out-of-Distribution Detection
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Libraries
Use these libraries to find Out-of-Distribution Detection models and implementationsDatasets
CIFAR-10
CIFAR-100
Fashion-MNIST
SST
STL-10
iSUN
ImageNet-O
20 Newsgroups
Latest papers
Efficient Out-of-Distribution Detection of Melanoma with Wavelet-based Normalizing Flows
In this work, we aim at using these biases with domain-level knowledge of melanoma, to improve likelihood-based OOD detection of malignant images.
FrOoDo: Framework for Out-of-Distribution Detection
FrOoDo is an easy-to-use and flexible framework for Out-of-Distribution detection tasks in digital pathology.
A knee cannot have lung disease: out-of-distribution detection with in-distribution voting using the medical example of chest X-ray classification
Deep learning models are being applied to more and more use cases with astonishing success stories, but how do they perform in the real world?
XOOD: Extreme Value Based Out-Of-Distribution Detection For Image Classification
Detecting out-of-distribution (OOD) data at inference time is crucial for many applications of machine learning.
Out-of-Distribution Detection with Semantic Mismatch under Masking
cure-lab/moodcat
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This paper proposes a novel out-of-distribution (OOD) detection framework named MoodCat for image classifiers.
CODiT: Conformal Out-of-Distribution Detection in Time-Series Data
kaustubhsridhar/time-series-ood
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Machine learning models are prone to making incorrect predictions on inputs that are far from the training distribution.
Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring Approach
Experiments on diverse real-world benchmarks demonstrate that the SRS method is well-suited for time-series OOD detection when compared to baseline methods.
Out of Distribution Detection via Neural Network Anchoring
Heteroscedasticity here refers to the fact that the optimal temperature parameter for each sample can be different, as opposed to conventional approaches that use the same value for the entire distribution.
Back to the Basics: Revisiting Out-of-Distribution Detection Baselines
cleanlab/ood-detection-benchmarks
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We study simple methods for out-of-distribution (OOD) image detection that are compatible with any already trained classifier, relying on only its predictions or learned representations.
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition
amazon-research/long-tailed-ood-detection
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However, in real-world applications, it is common for the training sets to have long-tailed distributions.
