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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 1–25 of 629 papers

TitleStatusHype
ZClassifier: Temperature Tuning and Manifold Approximation via KL Divergence on Logit SpaceCode0
Safe Domain Randomization via Uncertainty-Aware Out-of-Distribution Detection and Policy Adaptation—0
FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution DetectionCode0
Enclosing Prototypical Variational Autoencoder for Explainable Out-of-Distribution Detection—0
A Variational Information Theoretic Approach to Out-of-Distribution Detection—0
FindMeIfYouCan: Bringing Open Set metrics to near , far and farther Out-of-Distribution Object Detection—0
Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection—0
DIsoN: Decentralized Isolation Networks for Out-of-Distribution Detection in Medical Imaging—0
Evidential Spectrum-Aware Contrastive Learning for OOD Detection in Dynamic GraphsCode0
Logits-Based FinetuningCode2
Network Inversion for Uncertainty-Aware Out-of-Distribution Detection—0
Improving Out-of-Distribution Detection with Markov Logic Networks—0
Extremely Simple Multimodal Outlier Synthesis for Out-of-Distribution Detection and SegmentationCode2
GradPCA: Leveraging NTK Alignment for Reliable Out-of-Distribution Detection—0
SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps—0
Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selections and ApproximationsCode0
Humble your Overconfident Networks: Unlearning Overfitting via Sequential Monte Carlo Tempered Deep Ensembles—0
Unsupervised Out-of-Distribution Detection in Medical Imaging Using Multi-Exit Class Activation Maps and Feature MaskingCode0
Quantum Conflict Measurement in Decision Making for Out-of-Distribution Detection—0
Graph Synthetic Out-of-Distribution Exposure with Large Language Models—0
GLIP-OOD: Zero-Shot Graph OOD Detection with Graph Foundation Model—0
Can We Ignore Labels In Out of Distribution Detection?—0
Enhancing Out-of-Distribution Detection with Extended Logit Normalization—0
Revisiting Likelihood-Based Out-of-Distribution Detection by Modeling RepresentationsCode0
STOOD-X methodology: using statistical nonparametric test for OOD Detection Large-Scale datasets enhanced with explainability—0
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