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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 126–150 of 629 papers

TitleStatusHype
Towards Out-of-Distribution Detection in Vocoder Recognition via Latent Feature Reconstruction—0
Can Dense Connectivity Benefit Outlier Detection? An Odyssey with NAS—0
Effectiveness of Vision Language Models for Open-world Single Image Test Time Adaptation—0
When and How Does In-Distribution Label Help Out-of-Distribution Detection?Code0
WeiPer: OOD Detection using Weight Perturbations of Class Projections—0
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
Language-Enhanced Latent Representations for Out-of-Distribution Detection in Autonomous Driving—0
Out-of-distribution detection based on subspace projection of high-dimensional features output by the last convolutional layerCode0
Deep Metric Learning-Based Out-of-Distribution Detection with Synthetic Outlier Exposure—0
Feature Purified Transformer With Cross-level Feature Guiding Decoder For Multi-class OOD and Anomaly Deteciton—0
Out-of-distribution Detection in Medical Image Analysis: A survey—0
Unexplored Faces of Robustness and Out-of-Distribution: Covariate Shifts in Environment and Sensor DomainsCode1
Gradient-Regularized Out-of-Distribution DetectionCode0
Rethinking Out-of-Distribution Detection for Reinforcement Learning: Advancing Methods for Evaluation and DetectionCode0
VI-OOD: A Unified Representation Learning Framework for Textual Out-of-distribution DetectionCode0
Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector—0
On the Learnability of Out-of-distribution Detection—0
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