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Out of Distribution (OOD) Detection

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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.

논문

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Out of Distribution (OOD) Detection

논문

ZClassifier: Temperature Tuning and Manifold Approximation via KL Divergence on Logit Space

Safe Domain Randomization via Uncertainty-Aware Out-of-Distribution Detection and Policy Adaptation

FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution Detection

A Variational Information Theoretic Approach to Out-of-Distribution Detection

Enclosing Prototypical Variational Autoencoder for Explainable Out-of-Distribution Detection

FindMeIfYouCan: Bringing Open Set metrics to $\textit{near} $, $ \textit{far} $ and $\textit{farther}$ Out-of-Distribution Object Detection

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection

DIsoN: Decentralized Isolation Networks for Out-of-Distribution Detection in Medical Imaging

Evidential Spectrum-Aware Contrastive Learning for OOD Detection in Dynamic Graphs

Logits-Based Finetuning

Network Inversion for Uncertainty-Aware Out-of-Distribution Detection

Improving Out-of-Distribution Detection with Markov Logic Networks

Extremely Simple Multimodal Outlier Synthesis for Out-of-Distribution Detection and Segmentation

SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selections and Approximations

GradPCA: Leveraging NTK Alignment for Reliable Out-of-Distribution Detection

Humble your Overconfident Networks: Unlearning Overfitting via Sequential Monte Carlo Tempered Deep Ensembles

Unsupervised Out-of-Distribution Detection in Medical Imaging Using Multi-Exit Class Activation Maps and Feature Masking

Quantum Conflict Measurement in Decision Making for Out-of-Distribution Detection

GLIP-OOD: Zero-Shot Graph OOD Detection with Graph Foundation Model

Graph Synthetic Out-of-Distribution Exposure with Large Language Models

Can We Ignore Labels In Out of Distribution Detection?

Enhancing Out-of-Distribution Detection with Extended Logit Normalization

Revisiting Likelihood-Based Out-of-Distribution Detection by Modeling Representations

STOOD-X methodology: using statistical nonparametric test for OOD Detection Large-Scale datasets enhanced with explainability

Few-Shot Graph Out-of-Distribution Detection with LLMs

Extremely Simple Out-of-distribution Detection for Audio-visual Generalized Zero-shot Learning

VisTa: Visual-contextual and Text-augmented Zero-shot Object-level OOD Detection

ProHOC: Probabilistic Hierarchical Out-of-Distribution Classification via Multi-Depth Networks

Benchmarking Post-Hoc Unknown-Category Detection in Food Recognition

Leveraging Perturbation Robustness to Enhance Out-of-Distribution Detection

OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detection

OODD: Test-time Out-of-Distribution Detection with Dynamic Dictionary

OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy Prediction

AdaSCALE: Adaptive Scaling for OOD Detection

Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning Policies