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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 181–190 of 629 papers

TitleStatusHype
Harnessing Large Language and Vision-Language Models for Robust Out-of-Distribution Detection—0
Deep Neural Network Identification of Limnonectes Species and New Class Detection Using Image Data—0
Deep Metric Learning-Based Out-of-Distribution Detection with Synthetic Outlier Exposure—0
Benchmark for Out-of-Distribution Detection in Deep Reinforcement Learning—0
Deep Hybrid Models for Out-of-Distribution Detection—0
Decomposing Texture and Semantics for Out-of-distribution Detection—0
Decomposing Representations for Deterministic Uncertainty Estimation—0
A deep learning framework for the detection and quantification of drusen and reticular pseudodrusen on optical coherence tomography—0
Beyond Perceptual Distances: Rethinking Disparity Assessment for Out-of-Distribution Detection with Diffusion Models—0
Data Invariants to Understand Unsupervised Out-of-Distribution Detection—0
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