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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 551–560 of 629 papers

TitleStatusHype
Harnessing Large Language and Vision-Language Models for Robust Out-of-Distribution Detection—0
A statistical framework for efficient out of distribution detection in deep neural networks—0
Exploiting Diffusion Prior for Out-of-Distribution Detection—0
Evaluation of Out-of-Distribution Detection Performance of Self-Supervised Learning in a Controllable Environment—0
High- and Low-level image component decomposition using VAEs for improved reconstruction and anomaly detection—0
Histogram- and Diffusion-Based Medical Out-of-Distribution Detection—0
Holistic Sentence Embeddings for Better Out-of-Distribution Detection—0
HOOD: Real-Time Human Presence and Out-of-Distribution Detection Using FMCW Radar—0
How Does Fine-Tuning Impact Out-of-Distribution Detection for Vision-Language Models?—0
Evaluating the Practical Utility of Confidence-score based Techniques for Unsupervised Open-world Classification—0
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