Out-of-Distribution Detection
145 papers with code • 40 benchmarks • 15 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
CIFAR10
ImageNet-O
Most implemented papers
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
clovaai/CutMix-PyTorch
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ICCV 2019
Regional dropout strategies have been proposed to enhance the performance of convolutional neural network classifiers.
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
We consider the two related problems of detecting if an example is misclassified or out-of-distribution.
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
facebookresearch/odin
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ICLR 2018
We show in a series of experiments that ODIN is compatible with diverse network architectures and datasets.
Deep Anomaly Detection with Outlier Exposure
hendrycks/outlier-exposure
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ICLR 2019
We also analyze the flexibility and robustness of Outlier Exposure, and identify characteristics of the auxiliary dataset that improve performance.
Likelihood Ratios for Out-of-Distribution Detection
google-research/google-research
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NeurIPS 2019
We propose a likelihood ratio method for deep generative models which effectively corrects for these confounding background statistics.
Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices
VectorInstitute/gram-ood-detection
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We find that characterizing activity patterns by Gram matrices and identifying anomalies in gram matrix values can yield high OOD detection rates.
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
pokaxpoka/deep_Mahalanobis_detector
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NeurIPS 2018
Detecting test samples drawn sufficiently far away from the training distribution statistically or adversarially is a fundamental requirement for deploying a good classifier in many real-world machine learning applications.
Hierarchical VAEs Know What They Don't Know
Deep generative models have been demonstrated as state-of-the-art density estimators.
A Flexible and Adaptive Framework for Abstention Under Class Imbalance
Comparatively little attention was given to metrics such as area-under-the-curve or Cohen's Kappa, which are extremely relevant for imbalanced datasets.
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
hendrycks/ss-ood
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NeurIPS 2019
Self-supervision provides effective representations for downstream tasks without requiring labels.
