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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 391–400 of 629 papers

TitleStatusHype
DisCoPatch: Taming Adversarially-driven Batch Statistics for Improved Out-of-Distribution Detection—0
OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation—0
OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification—0
Dimensionality-induced information loss of outliers in deep neural networks—0
Diffusion based Semantic Outlier Generation via Nuisance Awareness for Out-of-Distribution Detection—0
WeShort: Out-of-distribution Detection With Weak Shortcut structure—0
DICE: A Simple Sparsification Method for Out-of-distribution Detection—0
Open-Set Semi-Supervised Object Detection—0
Open-World Continual Learning: Unifying Novelty Detection and Continual Learning—0
Benchmark for Out-of-Distribution Detection in Deep Reinforcement Learning—0
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