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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 326–350 of 629 papers

TitleStatusHype
Detecting and Learning Out-of-Distribution Data in the Open world: Algorithm and Theory—0
A Metacognitive Approach to Out-of-Distribution Detection for Segmentation—0
Out-of-Distribution Detection by Leveraging Between-Layer Transformation SmoothnessCode0
OOD Aware Supervised Contrastive Learning—0
ImageNet-OOD: Deciphering Modern Out-of-Distribution Detection AlgorithmsCode0
Can Pre-trained Networks Detect Familiar Out-of-Distribution Data?Code0
Meta OOD Learning for Continuously Adaptive OOD Detection—0
On the detection of Out-Of-Distribution samples in Multiple Instance LearningCode0
Unsupervised Out-of-Distribution Detection by Restoring Lossy Inputs with Variational AutoencoderCode0
Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification—0
Enhancing Automated and Early Detection of Alzheimer's Disease Using Out-Of-Distribution Detection—0
Improving Out-of-Distribution Detection in Echocardiographic View Classication through Enhancing Semantic Features—0
SupEuclid: Extremely Simple, High Quality OoD Detection with Supervised Contrastive Learning and Euclidean Distance—0
From Global to Local: Multi-scale Out-of-distribution DetectionCode0
Out-of-distribution multi-view auto-encoders for prostate cancer lesion detection—0
Building Safe and Reliable AI systems for Safety Critical Tasks with Vision-Language Processing—0
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization—0
Three Factors to Improve Out-of-Distribution Detection—0
MIM-OOD: Generative Masked Image Modelling for Out-of-Distribution Detection in Medical Images—0
HOOD: Real-Time Human Presence and Out-of-Distribution Detection Using FMCW Radar—0
General-Purpose Multi-Modal OOD Detection Framework—0
Large Class Separation is not what you need for Relational Reasoning-based OOD DetectionCode0
Random-Set Neural Networks (RS-NN)—0
Image Background Serves as Good Proxy for Out-of-distribution Data—0
Limitations of Out-of-Distribution Detection in 3D Medical Image Segmentation—0
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