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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 301–350 of 629 papers

TitleStatusHype
General-Purpose Multi-Modal OOD Detection Framework—0
GLIP-OOD: Zero-Shot Graph OOD Detection with Graph Foundation Model—0
GradPCA: Leveraging NTK Alignment for Reliable Out-of-Distribution Detection—0
Graph Synthetic Out-of-Distribution Exposure with Large Language Models—0
GRODIN: Improved Large-Scale Out-of-Domain detection via Back-propagation—0
GROOD: Gradient-Aware Out-of-Distribution Detection—0
Harnessing Large Language and Vision-Language Models for Robust Out-of-Distribution Detection—0
High- and Low-level image component decomposition using VAEs for improved reconstruction and anomaly detection—0
Histogram- and Diffusion-Based Medical Out-of-Distribution Detection—0
Holistic Sentence Embeddings for Better Out-of-Distribution Detection—0
HOOD: Real-Time Human Presence and Out-of-Distribution Detection Using FMCW Radar—0
How Does Fine-Tuning Impact Out-of-Distribution Detection for Vision-Language Models?—0
How to Enable Uncertainty Estimation in Proximal Policy Optimization—0
How Useful are Gradients for OOD Detection Really?—0
Humble your Overconfident Networks: Unlearning Overfitting via Sequential Monte Carlo Tempered Deep Ensembles—0
Hyperbolic Metric Learning for Visual Outlier Detection—0
Hypercone Assisted Contour Generation for Out-of-Distribution Detection—0
HyperMix: Out-of-Distribution Detection and Classification in Few-Shot Settings—0
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection—0
Identifying Out-of-Distribution Samples in Real-Time for Safety-Critical 2D Object Detection with Margin Entropy Loss—0
Out-of-distribution detection for regression tasks: parameter versus predictor entropy—0
PAWS-VMK: A Unified Approach To Semi-Supervised Learning And Out-of-Distribution Detection—0
Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training—0
Improving Out-of-Distribution Detection with Markov Logic Networks—0
Improving Training and Inference of Face Recognition Models via Random Temperature Scaling—0
Linking Neural Collapse and L2 Normalization with Improved Out-of-Distribution Detection in Deep Neural Networks—0
Informative Outlier Matters: Robustifying Out-of-distribution Detection Using Outlier Mining—0
Instance-Aware Observer Network for Out-of-Distribution Object Segmentation—0
Interpretable Out-Of-Distribution Detection Using Pattern Identification—0
Interpreting deep learning output for out-of-distribution detection—0
Intra-class Mixup for Out-of-Distribution Detection—0
Is it all a cluster game? -- Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space—0
Is Out-of-Distribution Detection Learnable?—0
Joint Distribution across Representation Space for Out-of-Distribution Detection—0
Joint Learning of Domain Classification and Out-of-Domain Detection with Dynamic Class Weighting for Satisficing False Acceptance Rates—0
Mitigating Hallucinations in YOLO-based Object Detection Models: A Revisit to Out-of-Distribution Detection—0
Mitigating the Modality Gap: Few-Shot Out-of-Distribution Detection with Multi-modal Prototypes and Image Bias Estimation—0
Mitral Regurgitation Recognition based on Unsupervised Out-of-Distribution Detection with Residual Diffusion Amplification—0
Model2Detector:Widening the Information Bottleneck for Out-of-Distribution Detection using a Handful of Gradient Steps—0
Model-free Test Time Adaptation for Out-Of-Distribution Detection—0
MOG: Molecular Out-of-distribution Generation with Energy-based Models—0
Multidimensional Uncertainty Quantification for Deep Neural Networks—0
Multi-layer Radial Basis Function Networks for Out-of-distribution Detection—0
Multiple Testing Framework for Out-of-Distribution Detection—0
Shifting Transformation Learning for Out-of-Distribution Detection—0
NADS: Neural Architecture Distribution Search for Uncertainty Awareness—0
Natural Attribute-based Shift Detection—0
Negative Sampling in Variational Autoencoders—0
Network Inversion for Uncertainty-Aware Out-of-Distribution Detection—0
Neural Network Out-of-Distribution Detection for Regression Tasks—0
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