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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 526–550 of 629 papers

TitleStatusHype
BED: Bi-Encoder-Based Detectors for Out-of-Distribution DetectionCode0
Outlier Exposure with Confidence Control for Out-of-Distribution DetectionCode0
Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selections and ApproximationsCode0
Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer OutputCode0
Non-Linear Outlier Synthesis for Out-of-Distribution DetectionCode0
Back to the Basics: Revisiting Out-of-Distribution Detection BaselinesCode0
Detecting Out-of-distribution Data through In-distribution Class PriorCode0
No True State-of-the-Art? OOD Detection Methods are Inconsistent across DatasetsCode0
WAIC, but Why? Generative Ensembles for Robust Anomaly DetectionCode0
Enhancing OOD Detection Using Latent DiffusionCode0
Kernel PCA for Out-of-Distribution DetectionCode0
On Out-of-Distribution Detection for Audio with Deep Nearest NeighborsCode0
ITP: Instance-Aware Test Pruning for Out-of-Distribution DetectionCode0
On the detection of Out-Of-Distribution samples in Multiple Instance LearningCode0
Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain DetectionCode0
Input complexity and out-of-distribution detection with likelihood-based generative modelsCode0
On the Importance of Regularisation & Auxiliary Information in OOD DetectionCode0
On out-of-distribution detection with Bayesian neural networksCode0
Improving Variational Autoencoder based Out-of-Distribution Detection for Embedded Real-time ApplicationsCode0
On the Practicality of Deterministic Epistemic UncertaintyCode0
On the Usefulness of Deep Ensemble Diversity for Out-of-Distribution DetectionCode0
Improving Out-of-Distribution Detection by Combining Existing Post-hoc MethodsCode0
Solving Sample-Level Out-of-Distribution Detection on 3D Medical ImagesCode0
SOOD-ImageNet: a Large-Scale Dataset for Semantic Out-Of-Distribution Image Classification and Semantic SegmentationCode0
Improving Confident-Classifiers For Out-of-distribution DetectionCode0
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