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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 201–250 of 629 papers

TitleStatusHype
FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution DetectionCode0
Open-World Lifelong Graph LearningCode0
On the Importance of Regularisation & Auxiliary Information in OOD DetectionCode0
Back to the Basics: Revisiting Out-of-Distribution Detection BaselinesCode0
CVAD: A generic medical anomaly detector based on Cascade VAECode0
Fast Decision Boundary based Out-of-Distribution DetectorCode0
On the Practicality of Deterministic Epistemic UncertaintyCode0
On Out-of-Distribution Detection for Audio with Deep Nearest NeighborsCode0
Evidential Spectrum-Aware Contrastive Learning for OOD Detection in Dynamic GraphsCode0
Out-of-Distribution Detection by Leveraging Between-Layer Transformation SmoothnessCode0
BED: Bi-Encoder-Based Detectors for Out-of-Distribution DetectionCode0
Contrastive Learning for OOD in Object detectionCode0
Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring ApproachCode0
On the detection of Out-Of-Distribution samples in Multiple Instance LearningCode0
On the Usefulness of Deep Ensemble Diversity for Out-of-Distribution DetectionCode0
Outlier Synthesis via Hamiltonian Monte Carlo for Out-of-Distribution DetectionCode0
Sample-dependent Adaptive Temperature Scaling for Improved CalibrationCode0
Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution DetectionCode0
Contextual Out-of-Domain Utterance Handling With Counterfeit Data AugmentationCode0
NCDD: Nearest Centroid Distance Deficit for Out-Of-Distribution Detection in Gastrointestinal VisionCode0
AdaSCALE: Adaptive Scaling for OOD DetectionCode0
Distilling the Unknown to Unveil CertaintyCode0
Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric EnsemblesCode0
Non-Linear Outlier Synthesis 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
Confidence-based Out-of-Distribution Detection: A Comparative Study and AnalysisCode0
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
Multi-Label Out-of-Distribution Detection with Spectral Normalized Joint EnergyCode0
No True State-of-the-Art? OOD Detection Methods are Inconsistent across DatasetsCode0
Gradient-Regularized Out-of-Distribution DetectionCode0
Confidence-Aware and Self-Supervised Image Anomaly LocalisationCode0
Metric Learning and Adaptive Boundary for Out-of-Domain DetectionCode0
Concept-based Explanations for Out-Of-Distribution DetectorsCode0
Concept Matching with Agent for Out-of-Distribution DetectionCode0
Mining In-distribution Attributes in Outliers for Out-of-distribution DetectionCode0
Long-Tailed Out-of-Distribution Detection via Normalized Outlier Distribution AdaptationCode0
Harnessing Out-Of-Distribution Examples via Augmenting Content and StyleCode0
Long-Tailed Out-of-Distribution Detection: Prioritizing Attention to TailCode0
Efficient Out-of-Distribution Detection of Melanoma with Wavelet-based Normalizing FlowsCode0
Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer OutputCode0
Detecting Out-of-Distribution Through the Lens of Neural CollapseCode0
Revealing the Distributional Vulnerability of Discriminators by Implicit GeneratorsCode0
A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution DetectionCode0
LEGO-Learn: Label-Efficient Graph Open-Set LearningCode0
Semi-supervised novelty detection using ensembles with regularized disagreementCode0
Leveraging Perturbation Robustness to Enhance Out-of-Distribution DetectionCode0
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