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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
Understanding Softmax Confidence and Uncertainty—0
Understanding the properties and limitations of contrastive learning for Out-of-Distribution detection—0
Understanding the Role of Self-Supervised Learning in Out-of-Distribution Detection Task—0
Unified Out-Of-Distribution Detection: A Model-Specific Perspective—0
Unsupervised Approaches for Out-Of-Distribution Dermoscopic Lesion Detection—0
Unsupervised Evaluation of Out-of-distribution Detection: A Data-centric Perspective—0
Unsupervised Layer-wise Score Aggregation for Textual OOD Detection—0
Unsupervised Out-of-Distribution Detection with Batch Normalization—0
Using Semantic Information for Defining and Detecting OOD Inputs—0
VisTa: Visual-contextual and Text-augmented Zero-shot Object-level OOD Detection—0
WeiPer: OOD Detection using Weight Perturbations of Class Projections—0
WeShort: Out-of-distribution Detection With Weak Shortcut structure—0
What do we learn? Debunking the Myth of Unsupervised Outlier Detection—0
Who Needs Decoders? Efficient Estimation of Sequence-level Attributes—0
Why is the Mahalanobis Distance Effective for Anomaly Detection?—0
Why Should we Combine Training and Post-Training Methods for Out-of-Distribution Detection?—0
WiP Abstract : Robust Out-of-distribution Motion Detection and Localization in Autonomous CPS—0
Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector—0
Zero-shot Object-Level OOD Detection with Context-Aware Inpainting—0
[Re] A Reproduction of Ensemble Distribution DistillationCode0
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer EnsembleCode0
Transformer-based out-of-distribution detection for clinically safe segmentationCode0
Concept Matching with Agent for Out-of-Distribution DetectionCode0
Enhancing Few-Shot Out-of-Distribution Detection with Gradient Aligned Context OptimizationCode0
Efficient Out-of-Distribution Detection of Melanoma with Wavelet-based Normalizing FlowsCode0
Out-Of-Distribution Detection for Audio-visual Generalized Zero-Shot Learning: A General FrameworkCode0
Rethinking Out-of-Distribution Detection for Reinforcement Learning: Advancing Methods for Evaluation and DetectionCode0
Can Pre-trained Networks Detect Familiar Out-of-Distribution Data?Code0
Do Bayesian Variational Autoencoders Know What They Don't Know?Code0
Rethinking the Evaluation of Out-of-Distribution Detection: A Sorites ParadoxCode0
Revealing the Distributional Vulnerability of Discriminators by Implicit GeneratorsCode0
Revisiting Deep Ensemble for Out-of-Distribution Detection: A Loss Landscape PerspectiveCode0
Revisiting Energy-Based Model for Out-of-Distribution DetectionCode0
TrustGAN: Training safe and trustworthy deep learning models through generative adversarial networksCode0
Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image ModelsCode0
Revisiting Likelihood-Based Out-of-Distribution Detection by Modeling RepresentationsCode0
Boosting Out-of-Distribution Detection with Multiple Pre-trained ModelsCode0
A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection using Compounded CorruptionsCode0
Zero-Shot Out-of-Distribution Detection with Feature CorrelationsCode0
Revisit Overconfidence for OOD Detection: Reassigned Contrastive Learning with Adaptive Class-dependent ThresholdCode0
Revisit PCA-based Technique for Out-of-Distribution DetectionCode0
A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?Code0
Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and AccuracyCode0
Robust Out-of-Distribution Detection on Deep Probabilistic Generative ModelsCode0
Distribution Calibration for Out-of-Domain Detection with Bayesian ApproximationCode0
Mining In-distribution Attributes in Outliers for Out-of-distribution DetectionCode0
Metric Learning and Adaptive Boundary for Out-of-Domain DetectionCode0
Robust Representation via Dynamic Feature AggregationCode0
Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based LearningCode0
VI-OOD: A Unified Representation Learning Framework for Textual Out-of-distribution DetectionCode0
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