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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 351–375 of 629 papers

TitleStatusHype
Boundary Aware Learning for Out-of-distribution Detection—0
Multi-layer Radial Basis Function Networks for Out-of-distribution Detection—0
Dual Conditioned Diffusion Models for Out-Of-Distribution Detection: Application to Fetal Ultrasound Videos—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
Dual-Adapter: Training-free Dual Adaptation for Few-shot Out-of-Distribution Detection—0
Towards Out-of-Distribution Detection in Vocoder Recognition via Latent Feature Reconstruction—0
Double Descent Meets Out-of-Distribution Detection: Theoretical Insights and Empirical Analysis on the role of model complexity—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
DOSE3 : Diffusion-based Out-of-distribution detection on SE(3) trajectories—0
DOODLER: Determining Out-Of-Distribution Likelihood from Encoder Reconstructions—0
NODI: Out-Of-Distribution Detection with Noise from Diffusion—0
NoiER: An Approach for Training more Reliable Fine-TunedDownstream Task Models—0
DOI: Divergence-based Out-of-Distribution Indicators via Deep Generative Models—0
'No' Matters: Out-of-Distribution Detection in Multimodality Long Dialogue—0
A deep learning framework for the detection and quantification of drusen and reticular pseudodrusen on optical coherence tomography—0
No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection—0
No Shifted Augmentations (NSA): strong baselines for self-supervised Anomaly Detection—0
Towards Rigorous Design of OoD Detectors—0
Novelty Detection Via Blurring—0
Towards Textual Out-of-Domain Detection without In-Domain Labels—0
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