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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 301–350 of 629 papers

TitleStatusHype
YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution DetectionCode1
Block Selection Method for Using Feature Norm in Out-of-distribution DetectionCode1
Improving Training and Inference of Face Recognition Models via Random Temperature Scaling—0
Rethinking Out-of-Distribution Detection From a Human-Centric Perspective—0
Out-Of-Distribution Detection Is Not All You Need—0
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic SegmentationCode1
TrustGAN: Training safe and trustworthy deep learning models through generative adversarial networksCode0
Beyond Mahalanobis-Based Scores for Textual OOD Detection—0
Multi-Level Knowledge Distillation for Out-of-Distribution Detection in TextCode1
Diffusion Denoising Process for Perceptron Bias in Out-of-distribution DetectionCode0
Towards Realistic Out-of-Distribution Detection: A Novel Evaluation Framework for Improving Generalization in OOD DetectionCode0
Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile RobotCode1
Heatmap-based Out-of-Distribution DetectionCode1
A Benchmark for Out of Distribution Detection in Point Cloud 3D Semantic Segmentation—0
Estimating Soft Labels for Out-of-Domain Intent Detection—0
GOOD-D: On Unsupervised Graph Out-Of-Distribution DetectionCode1
Interpreting deep learning output for out-of-distribution detection—0
Understanding the properties and limitations of contrastive learning for Out-of-Distribution detection—0
A Theoretical Study on Solving Continual LearningCode1
Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation—0
On Out-of-Distribution Detection for Audio with Deep Nearest NeighborsCode0
Is Out-of-Distribution Detection Learnable?—0
Uncertainty-based Meta-Reinforcement Learning for Robust Radar Tracking—0
Falsehoods that ML researchers believe about OOD detection—0
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer EnsembleCode0
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 DetectionCode0
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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