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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 401–425 of 629 papers

TitleStatusHype
Beyond Mahalanobis-Based Scores for Textual OOD Detection—0
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
A Benchmark for Out of Distribution Detection in Point Cloud 3D Semantic Segmentation—0
Estimating Soft Labels for Out-of-Domain Intent Detection—0
Interpreting deep learning output for out-of-distribution detection—0
Understanding the properties and limitations of contrastive learning for Out-of-Distribution detection—0
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
Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel—0
An out-of-distribution discriminator based on Bayesian neural network epistemic uncertainty—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
A Novel Explainable Out-of-Distribution Detection Approach for Spiking Neural NetworksCode0
Out-of-Distribution Detection and Selective Generation for Conditional Language Models—0
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