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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 141–150 of 629 papers

TitleStatusHype
Out-of-distribution detection based on subspace projection of high-dimensional features output by the last convolutional layerCode0
Deep Metric Learning-Based Out-of-Distribution Detection with Synthetic Outlier Exposure—0
Feature Purified Transformer With Cross-level Feature Guiding Decoder For Multi-class OOD and Anomaly Deteciton—0
Out-of-distribution Detection in Medical Image Analysis: A survey—0
Unexplored Faces of Robustness and Out-of-Distribution: Covariate Shifts in Environment and Sensor DomainsCode1
Gradient-Regularized Out-of-Distribution DetectionCode0
Rethinking Out-of-Distribution Detection for Reinforcement Learning: Advancing Methods for Evaluation and DetectionCode0
VI-OOD: A Unified Representation Learning Framework for Textual Out-of-distribution DetectionCode0
Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector—0
On the Learnability of Out-of-distribution Detection—0
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