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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 451–500 of 629 papers

TitleStatusHype
Back to the Basics: Revisiting Out-of-Distribution Detection BaselinesCode0
Revisit Overconfidence for OOD Detection: Reassigned Contrastive Learning with Adaptive Class-dependent ThresholdCode0
Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion ImagesCode0
EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering—0
SHELS: Exclusive Feature Sets for Novelty Detection and Continual Learning Without Class BoundariesCode0
Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection—0
WeShort: Out-of-distribution Detection With Weak Shortcut structure—0
Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective—0
Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Space—0
Multiple Testing Framework for Out-of-Distribution Detection—0
Supervision Adaptation Balancing In-distribution Generalization and Out-of-distribution Detection—0
READ: Aggregating Reconstruction Error into Out-of-distribution Detection—0
Federated Learning with Uncertainty via Distilled Predictive Distributions—0
Out-of-Distribution Detection with Class Ratio Estimation—0
What do we learn? Debunking the Myth of Unsupervised Outlier Detection—0
Transformer-based out-of-distribution detection for clinically safe segmentationCode0
How Useful are Gradients for OOD Detection Really?—0
Robust Representation via Dynamic Feature AggregationCode0
KNN-Contrastive Learning for Out-of-Domain Intent Classification—0
Evaluating the Practical Utility of Confidence-score based Techniques for Unsupervised Open-world Classification—0
Learning by Erasing: Conditional Entropy based Transferable Out-Of-Distribution DetectionCode0
Metric Learning and Adaptive Boundary for Out-of-Domain DetectionCode0
Out-Of-Distribution Detection In Unsupervised Continual Learning—0
Full-Spectrum Out-of-Distribution Detection—0
Effective Out-of-Distribution Detection in Classifier Based on PEDCC-Loss—0
A deep learning framework for the detection and quantification of drusen and reticular pseudodrusen on optical coherence tomography—0
Out of Distribution Detection, Generalization, and Robustness Triangle with Maximum Probability Theorem—0
Towards Textual Out-of-Domain Detection without In-Domain Labels—0
No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection—0
Is it all a cluster game? -- Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space—0
Igeood: An Information Geometry Approach to Out-of-Distribution DetectionCode0
Concept-based Explanations for Out-Of-Distribution DetectorsCode0
Layer Adaptive Deep Neural Networks for Out-of-distribution DetectionCode0
Computer Aided Diagnosis and Out-of-Distribution Detection in Glaucoma Screening Using Color Fundus Photography—0
Model2Detector:Widening the Information Bottleneck for Out-of-Distribution Detection using a Handful of Gradient Steps—0
Out of Distribution Detection on ImageNet-OCode0
Self-Supervised Anomaly Detection by Self-Distillation and Negative SamplingCode0
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection—0
Deep Hybrid Models for Out-of-Distribution Detection—0
Boundary Aware Learning for Out-of-distribution Detection—0
Energy-bounded Learning for Robust Models of Code—0
Benchmark for Out-of-Distribution Detection in Deep Reinforcement Learning—0
STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data—0
Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models—0
Decomposing Representations for Deterministic Uncertainty Estimation—0
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection—0
Data Invariants to Understand Unsupervised Out-of-Distribution Detection—0
Unsupervised Approaches for Out-Of-Distribution Dermoscopic Lesion Detection—0
Out of distribution detection for skin and malaria images—0
kFolden: k-Fold Ensemble for Out-Of-Distribution Detection—0
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