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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 176–200 of 629 papers

TitleStatusHype
Unsupervised Out-of-Distribution Detection by Restoring Lossy Inputs with Variational AutoencoderCode0
On Out-of-Distribution Detection for Audio with Deep Nearest NeighborsCode0
Being a Bit Frequentist Improves Bayesian Neural NetworksCode0
Enhancing OOD Detection Using Latent DiffusionCode0
BED: Bi-Encoder-Based Detectors for Out-of-Distribution DetectionCode0
Non-Linear Outlier Synthesis for Out-of-Distribution DetectionCode0
Analysis of Confident-Classifiers for Out-of-distribution DetectionCode0
No True State-of-the-Art? OOD Detection Methods are Inconsistent across DatasetsCode0
On the detection of Out-Of-Distribution samples in Multiple Instance LearningCode0
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
NCDD: Nearest Centroid Distance Deficit for Out-Of-Distribution Detection in Gastrointestinal VisionCode0
Back to the Basics: Revisiting Out-of-Distribution Detection BaselinesCode0
Metric Learning and Adaptive Boundary for Out-of-Domain DetectionCode0
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
Out-of-Distribution Detection based on In-Distribution Data Patterns Memorization with Modern Hopfield EnergyCode0
Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution DetectionCode0
Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection In Task Oriented DialogCode0
Contextual Out-of-Domain Utterance Handling With Counterfeit Data AugmentationCode0
AdaSCALE: Adaptive Scaling for OOD DetectionCode0
Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric EnsemblesCode0
Leveraging Perturbation Robustness to Enhance Out-of-Distribution DetectionCode0
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