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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 326–350 of 629 papers

TitleStatusHype
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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