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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 131–140 of 629 papers

TitleStatusHype
Concept Matching with Agent for Out-of-Distribution DetectionCode0
Enhancing Near OOD Detection in Prompt Learning: Maximum Gains, Minimal Costs—0
Dual-Adapter: Training-free Dual Adaptation for Few-shot Out-of-Distribution Detection—0
Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification—0
Embedding Trajectory for Out-of-Distribution Detection in Mathematical ReasoningCode1
Out-of-Distribution Detection with a Single Unconditional Diffusion ModelCode1
A Unified Approach Towards Active Learning and Out-of-Distribution Detection—0
Energy-based Hopfield Boosting for Out-of-Distribution DetectionCode1
Multi-Label Out-of-Distribution Detection with Spectral Normalized Joint EnergyCode0
Out-of-distribution detection based on subspace projection of high-dimensional features output by the last convolutional layerCode0
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