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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 551–575 of 629 papers

TitleStatusHype
Energy-based Out-of-distribution Detection for Multi-label Classification—0
Multidimensional Uncertainty-Aware Evidential Neural NetworksCode1
MASKER: Masked Keyword Regularization for Reliable Text ClassificationCode1
Exploring Vicinal Risk Minimization for Lightweight Out-of-Distribution Detection—0
Semi-supervised novelty detection using ensembles with regularized disagreementCode0
Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic SegmentationCode1
Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD DetectionCode1
Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features—0
The Hidden Uncertainty in a Neural Networks Activations—0
Improved Contrastive Divergence Training of Energy Based ModelsCode1
A Deep Generative Distance-Based Classifier for Out-of-Domain Detection with Mahalanobis Space—0
OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification—0
Feature Space Singularity for Out-of-Distribution DetectionCode1
Evaluation of Out-of-Distribution Detection Performance of Self-Supervised Learning in a Controllable Environment—0
Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD Detection On Medical Tabular DataCode1
Out-of-distribution detection for regression tasks: parameter versus predictor entropy—0
Uncertainty Aware Semi-Supervised Learning on Graph DataCode1
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution DataCode1
Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution ExamplesCode0
Learn what you can't learn: Regularized Ensembles for Transductive out-of-distribution detection—0
Informative Outlier Matters: Robustifying Out-of-distribution Detection Using Outlier Mining—0
An Algorithm for Out-Of-Distribution Attack to Neural Network EncoderCode0
FOOD: Fast Out-Of-Distribution DetectorCode1
Certifiably Adversarially Robust Detection of Out-of-Distribution DataCode1
Contrastive Training for Improved Out-of-Distribution Detection—0
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