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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 326–350 of 629 papers

TitleStatusHype
UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood LearningCode0
An out-of-distribution discriminator based on Bayesian neural network epistemic uncertainty—0
Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel—0
Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder—0
Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based LearningCode0
Pseudo-OOD training for robust language models—0
Holistic Sentence Embeddings for Better Out-of-Distribution Detection—0
Exploiting Mixed Unlabeled Data for Detecting Samples of Seen and Unseen Out-of-Distribution Classes—0
OpenOOD: Benchmarking Generalized Out-of-Distribution DetectionCode0
How to Enable Uncertainty Estimation in Proximal Policy Optimization—0
Your Out-of-Distribution Detection Method is Not Robust!Code1
A Novel Explainable Out-of-Distribution Detection Approach for Spiking Neural NetworksCode0
Out-of-Distribution Detection and Selective Generation for Conditional Language Models—0
Out-of-Distribution Detection for LiDAR-based 3D Object Detection—0
Out-of-Distribution Detection with Hilbert-Schmidt Independence OptimizationCode1
Raising the Bar on the Evaluation of Out-of-Distribution Detection—0
Linking Neural Collapse and L2 Normalization with Improved Out-of-Distribution Detection in Deep Neural Networks—0
Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection—0
Distribution Calibration for Out-of-Domain Detection with Bayesian ApproximationCode0
Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification—0
CAN bus intrusion detection based on auxiliary classifier GAN and out-of-distribution detectionCode1
Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training—0
Identifying Out-of-Distribution Samples in Real-Time for Safety-Critical 2D Object Detection with Margin Entropy Loss—0
Probing Contextual Diversity for Dense Out-of-Distribution DetectionCode0
Open-Set Semi-Supervised Object Detection—0
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