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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 226–250 of 629 papers

TitleStatusHype
Out-of-distribution multi-view auto-encoders for prostate cancer lesion detection—0
Building Safe and Reliable AI systems for Safety Critical Tasks with Vision-Language Processing—0
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization—0
Three Factors to Improve Out-of-Distribution Detection—0
MIM-OOD: Generative Masked Image Modelling for Out-of-Distribution Detection in Medical Images—0
HOOD: Real-Time Human Presence and Out-of-Distribution Detection Using FMCW Radar—0
General-Purpose Multi-Modal OOD Detection Framework—0
Large Class Separation is not what you need for Relational Reasoning-based OOD DetectionCode0
Random-Set Neural Networks (RS-NN)—0
Unsupervised 3D out-of-distribution detection with latent diffusion modelsCode1
Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic AssemblyCode1
Image Background Serves as Good Proxy for Out-of-distribution Data—0
Beyond AUROC & co. for evaluating out-of-distribution detection performanceCode1
Limitations of Out-of-Distribution Detection in 3D Medical Image Segmentation—0
Learnability and Algorithm for Continual LearningCode1
Balanced Energy Regularization Loss for Out-of-distribution DetectionCode1
OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution DetectionCode1
BED: Bi-Encoder-Based Detectors for Out-of-Distribution DetectionCode0
Towards Rigorous Design of OoD Detectors—0
How Does Fine-Tuning Impact Out-of-Distribution Detection for Vision-Language Models?—0
Conservative Prediction via Data-Driven Confidence MinimizationCode0
Exploring Simple, High Quality Out-of-Distribution Detection with L2 Normalization—0
A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution DetectionCode0
LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt LearningCode1
In or Out? Fixing ImageNet Out-of-Distribution Detection EvaluationCode1
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