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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 201–225 of 629 papers

TitleStatusHype
On the Importance of Regularisation & Auxiliary Information in OOD DetectionCode0
Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution DetectionCode0
Contextual Out-of-Domain Utterance Handling With Counterfeit Data AugmentationCode0
Metric Learning and Adaptive Boundary for Out-of-Domain DetectionCode0
CVAD: A generic medical anomaly detector based on Cascade VAECode0
Fast Decision Boundary based Out-of-Distribution DetectorCode0
AdaSCALE: Adaptive Scaling for OOD DetectionCode0
Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric EnsemblesCode0
Mining In-distribution Attributes in Outliers for Out-of-distribution DetectionCode0
AUTO: Adaptive Outlier Optimization for Test-Time OOD DetectionCode0
Conservative Prediction via Data-Driven Confidence MinimizationCode0
Enhancing Out-of-Distribution Detection in Medical Imaging with Normalizing FlowsCode0
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer EnsembleCode0
Long-Tailed Out-of-Distribution Detection: Prioritizing Attention to TailCode0
Confidence-based Out-of-Distribution Detection: A Comparative Study and AnalysisCode0
Open-World Lifelong Graph LearningCode0
Being a Bit Frequentist Improves Bayesian Neural NetworksCode0
Out-of-distribution detection based on subspace projection of high-dimensional features output by the last convolutional layerCode0
Unsupervised Out-of-Distribution Detection by Restoring Lossy Inputs with Variational AutoencoderCode0
From Global to Local: Multi-scale Out-of-distribution DetectionCode0
Out-of-Distribution Detection for Medical Applications: Guidelines for Practical EvaluationCode0
Out-of-distribution Detection in Classifiers via GenerationCode0
Enhancing Few-Shot Out-of-Distribution Detection with Gradient Aligned Context OptimizationCode0
Out-Of-Distribution Detection for Audio-visual Generalized Zero-Shot Learning: A General FrameworkCode0
Long-Tailed Out-of-Distribution Detection via Normalized Outlier Distribution AdaptationCode0
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