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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 201–250 of 629 papers

TitleStatusHype
Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection—0
FARE: A Deep Learning-Based Framework for Radar-based Face Recognition and Out-of-distribution Detection—0
kFolden: k-Fold Ensemble for Out-Of-Distribution Detection—0
Falsehoods that ML researchers believe about OOD detection—0
Curved Geometric Networks for Visual Anomaly Recognition—0
BAM: Box Abstraction Monitors for Real-time OoD Detection in Object Detection—0
KNN-Contrastive Learning for Out-of-Domain Intent Classification—0
Feature Purified Transformer With Cross-level Feature Guiding Decoder For Multi-class OOD and Anomaly Deteciton—0
Extremely Simple Out-of-distribution Detection for Audio-visual Generalized Zero-shot Learning—0
Few-Shot Graph Out-of-Distribution Detection with LLMs—0
FindMeIfYouCan: Bringing Open Set metrics to near , far and farther Out-of-Distribution Object Detection—0
Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification—0
Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification—0
Exploring Vicinal Risk Minimization for Lightweight Out-of-Distribution Detection—0
COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification—0
Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation—0
Free Lunch for Generating Effective Outlier Supervision—0
FROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection—0
Exploring Large Language Models for Multi-Modal Out-of-Distribution Detection—0
Deep Metric Learning-Based Out-of-Distribution Detection with Synthetic Outlier Exposure—0
Controlling Neural Collapse Enhances Out-of-Distribution Detection and Transfer Learning—0
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL—0
Exploring Covariate and Concept Shift for Detection and Calibration of Out-of-Distribution Data—0
Exploring Covariate and Concept Shift for Detection and Confidence Calibration of Out-of-Distribution Data—0
Contrastive Training for Improved Out-of-Distribution Detection—0
Exploiting Mixed Unlabeled Data for Detecting Samples of Seen and Unseen Out-of-Distribution Classes—0
Exploiting Diffusion Prior for Out-of-Distribution Detection—0
Evaluation of Out-of-Distribution Detection Performance of Self-Supervised Learning in a Controllable Environment—0
General-Purpose Multi-Modal OOD Detection Framework—0
GLIP-OOD: Zero-Shot Graph OOD Detection with Graph Foundation Model—0
A Deep Generative Distance-Based Classifier for Out-of-Domain Detection with Mahalanobis Space—0
Joint Distribution across Representation Space for Out-of-Distribution Detection—0
Joint Learning of Domain Classification and Out-of-Domain Detection with Dynamic Class Weighting for Satisficing False Acceptance Rates—0
Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing—0
Label Smoothed Embedding Hypothesis for Out-of-Distribution Detection—0
Graph Synthetic Out-of-Distribution Exposure with Large Language Models—0
GRODIN: Improved Large-Scale Out-of-Domain detection via Back-propagation—0
GROOD: Gradient-Aware Out-of-Distribution Detection—0
Meta Learning Low Rank Covariance Factors for Energy Based Deterministic Uncertainty—0
Evaluating the Practical Utility of Confidence-score based Techniques for Unsupervised Open-world Classification—0
Estimating Soft Labels for Out-of-Domain Intent Detection—0
EPA: Neural Collapse Inspired Robust Out-of-Distribution Detector—0
Interpretable Out-Of-Distribution Detection Using Pattern Identification—0
High- and Low-level image component decomposition using VAEs for improved reconstruction and anomaly detection—0
Entropic Issues in Likelihood-Based OOD Detection—0
Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification—0
HOOD: Real-Time Human Presence and Out-of-Distribution Detection Using FMCW Radar—0
How Does Fine-Tuning Impact Out-of-Distribution Detection for Vision-Language Models?—0
A Variational Information Theoretic Approach to Out-of-Distribution Detection—0
Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU—0
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