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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 241–250 of 629 papers

TitleStatusHype
Beyond Perceptual Distances: Rethinking Disparity Assessment for Out-of-Distribution Detection with Diffusion Models—0
Uncertainty-Guided Appearance-Motion Association Network for Out-of-Distribution Action DetectionCode0
Enhancing Outlier Knowledge for Few-Shot Out-of-Distribution Detection with Extensible Local Prompts—0
SOOD-ImageNet: a Large-Scale Dataset for Semantic Out-Of-Distribution Image Classification and Semantic SegmentationCode0
TagOOD: A Novel Approach to Out-of-Distribution Detection via Vision-Language Representations and Class Center LearningCode0
Diffusion based Semantic Outlier Generation via Nuisance Awareness for Out-of-Distribution Detection—0
Long-Tailed Out-of-Distribution Detection: Prioritizing Attention to TailCode0
Out-Of-Distribution Detection for Audio-visual Generalized Zero-Shot Learning: A General FrameworkCode0
Mitral Regurgitation Recognition based on Unsupervised Out-of-Distribution Detection with Residual Diffusion Amplification—0
Towards Open-World Object-based Anomaly Detection via Self-Supervised Outlier SynthesisCode0
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