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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 301–325 of 629 papers

TitleStatusHype
Transformer-based out-of-distribution detection for clinically safe segmentationCode0
TrustGAN: Training safe and trustworthy deep learning models through generative adversarial networksCode0
On out-of-distribution detection with Bayesian neural networksCode0
Uncertainty-Guided Appearance-Motion Association Network for Out-of-Distribution Action DetectionCode0
UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood LearningCode0
Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel MachineCode0
Unsupervised Hybrid framework for ANomaly Detection (HAND) -- applied to Screening MammogramCode0
Task-agnostic Out-of-Distribution Detection Using Kernel Density EstimationCode0
Unsupervised Out-of-Distribution Detection by Maximum Classifier DiscrepancyCode0
Unsupervised Out-of-Distribution Detection in Medical Imaging Using Multi-Exit Class Activation Maps and Feature MaskingCode0
Identity Curvature Laplace Approximation for Improved Out-of-Distribution DetectionCode0
VI-OOD: A Unified Representation Learning Framework for Textual Out-of-distribution DetectionCode0
WAIC, but Why? Generative Ensembles for Robust Anomaly DetectionCode0
Weak Distribution Detectors Lead to Stronger Generalizability of Vision-Language Prompt TuningCode0
What If the Input is Expanded in OOD Detection?Code0
When and How Does In-Distribution Label Help Out-of-Distribution Detection?Code0
XOOD: Extreme Value Based Out-Of-Distribution Detection For Image ClassificationCode0
ZClassifier: Temperature Tuning and Manifold Approximation via KL Divergence on Logit SpaceCode0
Zero-Shot Out-of-Distribution Detection with Feature CorrelationsCode0
Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models—0
Efficient Out-of-Distribution Detection via CVAE data Generation—0
Efficient Out-of-Distribution Detection Using Latent Space of β-VAE for Cyber-Physical Systems—0
Towards Few-shot Out-of-Distribution Detection—0
A Closer Look at the Learnability of Out-of-Distribution (OOD) Detection—0
MADOD: Generalizing OOD Detection to Unseen Domains via G-Invariance Meta-Learning—0
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