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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 576–600 of 629 papers

TitleStatusHype
Practical Evaluation of Out-of-Distribution Detection Methods for Image Classification—0
Energy-based Out-of-distribution Detection for Multi-label Classification—0
Exploring Vicinal Risk Minimization for Lightweight Out-of-Distribution Detection—0
Semi-supervised novelty detection using ensembles with regularized disagreementCode0
Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features—0
The Hidden Uncertainty in a Neural Networks Activations—0
OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification—0
A Deep Generative Distance-Based Classifier for Out-of-Domain Detection with Mahalanobis Space—0
Evaluation of Out-of-Distribution Detection Performance of Self-Supervised Learning in a Controllable Environment—0
Out-of-distribution detection for regression tasks: parameter versus predictor entropy—0
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
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
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
NADS: Neural Architecture Distribution Search for Uncertainty Awareness—0
Improving Calibration and Out-of-Distribution Detection in Medical Image Segmentation with Convolutional Neural NetworksCode0
Out-of-Distribution Detection in Multi-Label Datasets using Latent Space of β-VAE—0
Why is the Mahalanobis Distance Effective for Anomaly Detection?—0
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