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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 441–450 of 629 papers

TitleStatusHype
Uncertainty-based Meta-Reinforcement Learning for Robust Radar Tracking—0
Out-of-distribution multi-view auto-encoders for prostate cancer lesion detection—0
Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder—0
Out-of-domain Detection based on Generative Adversarial Network—0
A Baseline for Detecting Out-of-Distribution Examples in Image Captioning—0
Density of States Estimation for Out-of-Distribution Detection—0
Overcoming Shortcut Problem in VLM for Robust Out-of-Distribution Detection—0
Deep Neural Network Identification of Limnonectes Species and New Class Detection Using Image Data—0
PnPOOD : Out-Of-Distribution Detection for Text Classification via Plug andPlay Data Augmentation—0
Deep Metric Learning-Based Out-of-Distribution Detection with Synthetic Outlier Exposure—0
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