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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 231–240 of 629 papers

TitleStatusHype
Dimensionality-induced information loss of outliers in deep neural networks—0
Long-Tailed Out-of-Distribution Detection via Normalized Outlier Distribution AdaptationCode0
PViT: Prior-augmented Vision Transformer for Out-of-distribution DetectionCode0
What If the Input is Expanded in OOD Detection?Code0
GDDA: Semantic OOD Detection on Graphs under Covariate Shift via Score-Based Diffusion Models—0
LEGO-Learn: Label-Efficient Graph Open-Set LearningCode0
Adaptive Label Smoothing for Out-of-Distribution Detection—0
Tensor-Train Point Cloud Compression and Efficient Approximate Nearest-Neighbor Search—0
MetaOOD: Automatic Selection of OOD Detection Models—0
Unsupervised Hybrid framework for ANomaly Detection (HAND) -- applied to Screening MammogramCode0
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