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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 591–600 of 629 papers

TitleStatusHype
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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