Out-of-Distribution Detection
328 papers with code • 51 benchmarks • 23 datasets
Detect out-of-distribution or anomalous examples.
Benchmarks
These leaderboards are used to track progress in Out-of-Distribution Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Out-of-Distribution Detection models and implementationsDatasets
CIFAR-10
CIFAR-100
Fashion-MNIST
SST
STL-10
iSUN
Places365
20 Newsgroups
ImageNet-1k vs Textures
ImageNet-1k vs iNaturalist
Latest papers
Learning with Mixture of Prototypes for Out-of-Distribution Detection
jeff024/palm
•
•
To tackle these issues, we propose PrototypicAl Learning with a Mixture of prototypes (PALM) which models each class with multiple prototypes to capture the sample diversities, and learns more faithful and compact samples embeddings to enhance OOD detection.
Towards Optimal Feature-Shaping Methods for Out-of-Distribution Detection
Feature shaping refers to a family of methods that exhibit state-of-the-art performance for out-of-distribution (OOD) detection.
Out-of-Distribution Detection & Applications With Ablated Learned Temperature Energy
As deep neural networks become adopted in high-stakes domains, it is crucial to be able to identify when inference inputs are Out-of-Distribution (OOD) so that users can be alerted of likely drops in performance and calibration despite high confidence.
GOODAT: Towards Test-time Graph Out-of-Distribution Detection
ee1s/goodat
•
•
To identify and reject OOD samples with GNNs, recent studies have explored graph OOD detection, often focusing on training a specific model or modifying the data on top of a well-trained GNN.
Towards Reliable AI Model Deployments: Multiple Input Mixup for Out-of-Distribution Detection
With extensive experiments with CIFAR10 and CIFAR100 benchmarks that have been largely adopted in out-of-distribution detection fields, we have demonstrated our MIM shows comprehensively superior performance compared to the SOTA method.
Understanding normalization in contrastive representation learning and out-of-distribution detection
Our approach can be applied flexibly as an outlier exposure (OE) approach, where the out-of-distribution data is a huge collective of random images, or as a fully self-supervised learning approach, where the out-of-distribution data is self-generated by applying distribution-shifting transformations.
Out-of-Distribution Detection in Long-Tailed Recognition with Calibrated Outlier Class Learning
mala-lab/cocl
•
•
To this end, we introduce a novel calibrated outlier class learning (COCL) approach, in which 1) a debiased large margin learning method is introduced in the outlier class learning to distinguish OOD samples from both head and tail classes in the representation space and 2) an outlier-class-aware logit calibration method is defined to enhance the long-tailed classification confidence.
EAT: Towards Long-Tailed Out-of-Distribution Detection
stomach-ache/long-tailed-ood-detection
•
•
The main difficulty lies in distinguishing OOD data from samples belonging to the tail classes, as the ability of a classifier to detect OOD instances is not strongly correlated with its accuracy on the in-distribution classes.
Navigating Open Set Scenarios for Skeleton-based Action Recognition
kpeng9510/os-sar
•
•
In real-world scenarios, human actions often fall outside the distribution of training data, making it crucial for models to recognize known actions and reject unknown ones.
Likelihood-Aware Semantic Alignment for Full-Spectrum Out-of-Distribution Detection
Full-spectrum out-of-distribution (F-OOD) detection aims to accurately recognize in-distribution (ID) samples while encountering semantic and covariate shifts simultaneously.
