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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 526–550 of 629 papers

TitleStatusHype
Can multi-label classification networks know what they don’t know?Code1
Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family DistributionsCode1
MOOD: Multi-level Out-of-distribution DetectionCode1
Neural Mean Discrepancy for Efficient Out-of-Distribution Detection—0
Contrastive Out-of-Distribution Detection for Pretrained TransformersCode1
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions—0
OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation—0
The Compact Support Neural Network—0
Joint Distribution across Representation Space for Out-of-Distribution Detection—0
Out-of-Distribution Detection of Melanoma using Normalizing Flows—0
SSD: A Unified Framework for Self-Supervised Outlier DetectionCode1
Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learning—0
A statistical framework for efficient out of distribution detection in deep neural networks—0
Bayesian OOD detection with aleatoric uncertainty and outlier exposure—0
Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural NetworksCode1
Make Sure You're Unsure: A Framework for Verifying Probabilistic SpecificationsCode1
Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel MachineCode0
Hierarchical VAEs Know What They Don't KnowCode1
Label Smoothed Embedding Hypothesis for Out-of-Distribution Detection—0
Probabilistic Trust Intervals for Out of Distribution DetectionCode0
[Re] A Reproduction of Ensemble Distribution DistillationCode0
Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay BufferCode1
Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detection—0
Practical Evaluation of Out-of-Distribution Detection Methods for Image Classification—0
Bridging In- and Out-of-distribution Samples for Their Better Discriminability—0
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