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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 321–330 of 629 papers

TitleStatusHype
On Out-of-Distribution Detection for Audio with Deep Nearest NeighborsCode0
Is Out-of-Distribution Detection Learnable?—0
Uncertainty-based Meta-Reinforcement Learning for Robust Radar Tracking—0
Falsehoods that ML researchers believe about OOD detection—0
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer EnsembleCode0
UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood LearningCode0
An out-of-distribution discriminator based on Bayesian neural network epistemic uncertainty—0
Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel—0
Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder—0
Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based LearningCode0
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