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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 151–200 of 629 papers

TitleStatusHype
Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural NetworksCode1
Dream the Impossible: Outlier Imagination with Diffusion ModelsCode1
A Simple Fix to Mahalanobis Distance for Improving Near-OOD DetectionCode1
Heatmap-based Out-of-Distribution DetectionCode1
A framework for benchmarking class-out-of-distribution detection and its application to ImageNetCode1
How Good Are LLMs at Out-of-Distribution Detection?Code1
EAT: Towards Long-Tailed Out-of-Distribution DetectionCode1
CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say NoCode1
Contrastive Out-of-Distribution Detection for Pretrained TransformersCode1
A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection using Compounded CorruptionsCode0
Detecting semantic anomaliesCode0
OpenOOD: Benchmarking Generalized Out-of-Distribution DetectionCode0
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
Detecting Out-of-distribution Data through In-distribution Class PriorCode0
On the Practicality of Deterministic Epistemic UncertaintyCode0
On the Importance of Regularisation & Auxiliary Information in OOD DetectionCode0
A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?Code0
On the Usefulness of Deep Ensemble Diversity for Out-of-Distribution DetectionCode0
On the detection of Out-Of-Distribution samples in Multiple Instance LearningCode0
Advancing Out-of-Distribution Detection via Local NeuroplasticityCode0
Open-World Lifelong Graph LearningCode0
No True State-of-the-Art? OOD Detection Methods are Inconsistent across DatasetsCode0
Unsupervised Out-of-Distribution Detection by Restoring Lossy Inputs with Variational AutoencoderCode0
Enhancing OOD Detection Using Latent DiffusionCode0
Being a Bit Frequentist Improves Bayesian Neural NetworksCode0
Non-Linear Outlier Synthesis for Out-of-Distribution DetectionCode0
BED: Bi-Encoder-Based Detectors for Out-of-Distribution DetectionCode0
NCDD: Nearest Centroid Distance Deficit for Out-Of-Distribution Detection in Gastrointestinal VisionCode0
Analysis of Confident-Classifiers for Out-of-distribution DetectionCode0
On Out-of-Distribution Detection for Audio with Deep Nearest NeighborsCode0
CVAD: A generic medical anomaly detector based on Cascade VAECode0
An Algorithm for Out-Of-Distribution Attack to Neural Network EncoderCode0
Multi-Label Out-of-Distribution Detection with Spectral Normalized Joint EnergyCode0
Back to the Basics: Revisiting Out-of-Distribution Detection BaselinesCode0
Mining In-distribution Attributes in Outliers for Out-of-distribution DetectionCode0
Evidential Spectrum-Aware Contrastive Learning for OOD Detection in Dynamic GraphsCode0
Contrastive Learning for OOD in Object detectionCode0
Metric Learning and Adaptive Boundary for Out-of-Domain DetectionCode0
Outlier Synthesis via Hamiltonian Monte Carlo for Out-of-Distribution DetectionCode0
Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution DetectionCode0
LEGO-Learn: Label-Efficient Graph Open-Set LearningCode0
Contextual Out-of-Domain Utterance Handling With Counterfeit Data AugmentationCode0
Leveraging Perturbation Robustness to Enhance Out-of-Distribution DetectionCode0
AdaSCALE: Adaptive Scaling for OOD DetectionCode0
Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric EnsemblesCode0
Semi-supervised novelty detection using ensembles with regularized disagreementCode0
Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection In Task Oriented DialogCode0
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