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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 451–460 of 629 papers

TitleStatusHype
Deep Hybrid Models for Out-of-Distribution Detection—0
Practical Evaluation of Out-of-Distribution Detection Methods for Image Classification—0
Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks—0
A Survey on Out-of-Distribution Detection in NLP—0
Uncertainty-Estimation with Normalized Logits for Out-of-Distribution Detection—0
A Benchmark for Out of Distribution Detection in Point Cloud 3D Semantic Segmentation—0
Decomposing Texture and Semantics for Out-of-distribution Detection—0
Decomposing Representations for Deterministic Uncertainty Estimation—0
Beyond Perceptual Distances: Rethinking Disparity Assessment for Out-of-Distribution Detection with Diffusion Models—0
Understanding Failures in Out-of-Distribution Detection with Deep Generative Models—0
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