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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
Unexplored Faces of Robustness and Out-of-Distribution: Covariate Shifts in Environment and Sensor DomainsCode1
Dream the Impossible: Outlier Imagination with Diffusion ModelsCode1
A Simple Fix to Mahalanobis Distance for Improving Near-OOD DetectionCode1
FOOD: Fast Out-Of-Distribution DetectorCode1
A framework for benchmarking class-out-of-distribution detection and its application to ImageNetCode1
Heatmap-based Out-of-Distribution DetectionCode1
EAT: Towards Long-Tailed Out-of-Distribution DetectionCode1
CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say NoCode1
AdaptiveMix: Improving GAN Training via Feature Space ShrinkageCode1
A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection using Compounded CorruptionsCode0
Detecting semantic anomaliesCode0
NCDD: Nearest Centroid Distance Deficit for Out-Of-Distribution Detection in Gastrointestinal VisionCode0
Detecting Out-of-Distribution Through the Lens of Neural CollapseCode0
Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer OutputCode0
Boosting Out-of-Distribution Detection with Multiple Pre-trained ModelsCode0
A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution DetectionCode0
Multi-Label Out-of-Distribution Detection with Spectral Normalized Joint EnergyCode0
Detecting Out-of-distribution Data through In-distribution Class PriorCode0
A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?Code0
Metric Learning and Adaptive Boundary for Out-of-Domain DetectionCode0
Mining In-distribution Attributes in Outliers for Out-of-distribution DetectionCode0
Advancing Out-of-Distribution Detection via Local NeuroplasticityCode0
Unsupervised Out-of-Distribution Detection by Restoring Lossy Inputs with Variational AutoencoderCode0
Long-Tailed Out-of-Distribution Detection: Prioritizing Attention to TailCode0
Being a Bit Frequentist Improves Bayesian Neural NetworksCode0
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