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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 201–225 of 629 papers

TitleStatusHype
BAM: Box Abstraction Monitors for Real-time OoD Detection in Object Detection—0
Extremely Simple Out-of-distribution Detection for Audio-visual Generalized Zero-shot Learning—0
Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification—0
Exploring Vicinal Risk Minimization for Lightweight Out-of-Distribution Detection—0
FARE: A Deep Learning-Based Framework for Radar-based Face Recognition and Out-of-distribution Detection—0
Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection—0
Feature Density Estimation for Out-of-Distribution Detection via Normalizing Flows—0
Feature Purified Transformer With Cross-level Feature Guiding Decoder For Multi-class OOD and Anomaly Deteciton—0
COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification—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
Out-of-distribution detection for regression tasks: parameter versus predictor entropy—0
Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification—0
Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation—0
Exploring Large Language Models for Multi-Modal Out-of-Distribution Detection—0
Controlling Neural Collapse Enhances Out-of-Distribution Detection and Transfer Learning—0
Free Lunch for Generating Effective Outlier Supervision—0
FROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection—0
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection—0
Deep Metric Learning-Based Out-of-Distribution Detection with Synthetic Outlier Exposure—0
Full-Spectrum Out-of-Distribution Detection—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
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