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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 151–175 of 629 papers

TitleStatusHype
Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncodersCode1
Robust Out-of-distribution Detection for Neural NetworksCode1
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoderCode1
Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution DataCode1
Detecting Out-of-Distribution Examples with In-distribution Examples and Gram MatricesCode1
Out-of-domain Detection for Natural Language Understanding in Dialog SystemsCode1
Isotropy Maximization Loss and Entropic Score: Accurate, Fast, Efficient, Scalable, and Turnkey Neural Networks Out-of-Distribution Detection Based on The Principle of Maximum EntropyCode1
Likelihood Ratios for Out-of-Distribution DetectionCode1
Deep Anomaly Detection with Outlier ExposureCode1
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
A Variational Information Theoretic Approach to Out-of-Distribution Detection—0
Enclosing Prototypical Variational Autoencoder for Explainable 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
Network Inversion for Uncertainty-Aware Out-of-Distribution Detection—0
Improving Out-of-Distribution Detection with Markov Logic Networks—0
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
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