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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
Detecting Out-of-Distribution Examples with In-distribution Examples and Gram MatricesCode1
Why Should we Combine Training and Post-Training Methods for Out-of-Distribution Detection?—0
High- and Low-level image component decomposition using VAEs for improved reconstruction and anomaly detection—0
Novelty Detection Via Blurring—0
Efficacy of Pixel-Level OOD Detection for Semantic Segmentation—0
Out of distribution detection for intra-operative functional imaging—0
Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer OutputCode0
Unsupervised Out-of-Distribution Detection with Batch Normalization—0
Toward Metrics for Differentiating Out-of-Distribution SetsCode0
Out-of-distribution Detection in Classifiers via GenerationCode0
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