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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 551–600 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
Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks—0
A Critical Evaluation of Open-World Machine Learning—0
Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks—0
ATOM: Robustifying Out-of-distribution Detection Using Outlier MiningCode1
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?Code1
The Effect of Optimization Methods on the Robustness of Out-of-Distribution Detection Approaches—0
Task-agnostic Out-of-Distribution Detection Using Kernel Density EstimationCode0
Density of States Estimation for Out-of-Distribution Detection—0
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-CountsCode1
Why Normalizing Flows Fail to Detect Out-of-Distribution DataCode1
NADS: Neural Architecture Distribution Search for Uncertainty Awareness—0
Entropic Out-of-Distribution Detection: Seamless Detection of Unknown ExamplesCode1
Background Data Resampling for Outlier-Aware ClassificationCode1
Improving Calibration and Out-of-Distribution Detection in Medical Image Segmentation with Convolutional Neural NetworksCode0
Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncodersCode1
Robust Out-of-distribution Detection for Neural NetworksCode1
Out-of-Distribution Detection in Multi-Label Datasets using Latent Space of β-VAE—0
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoderCode1
Why is the Mahalanobis Distance Effective for Anomaly Detection?—0
Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution DataCode1
The Conditional Entropy Bottleneck—0
OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples—0
Efficient Out-of-Distribution Detection in Digital Pathology Using Multi-Head Convolutional Neural Networks—0
Detecting Out-of-Distribution Examples with Gram Matrices—0
Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection In Task Oriented DialogCode0
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