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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 601–610 of 629 papers

TitleStatusHype
The Conditional Entropy Bottleneck—0
OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples—0
Efficient Out-of-Distribution Detection in Digital Pathology Using Multi-Head Convolutional Neural Networks—0
Detecting Out-of-Distribution Examples with Gram Matrices—0
Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection In Task Oriented DialogCode0
Why Should we Combine Training and Post-Training Methods for Out-of-Distribution Detection?—0
Novelty Detection Via Blurring—0
High- and Low-level image component decomposition using VAEs for improved reconstruction and anomaly detection—0
Efficacy of Pixel-Level OOD Detection for Semantic Segmentation—0
Out of distribution detection for intra-operative functional imaging—0
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