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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 511–520 of 629 papers

TitleStatusHype
Exploring Simple, High Quality Out-of-Distribution Detection with L2 Normalization—0
Adaptive Label Smoothing for Out-of-Distribution Detection—0
Class-wise Thresholding for Robust Out-of-Distribution Detection—0
Situation Monitor: Diversity-Driven Zero-Shot Out-of-Distribution Detection using Budding Ensemble Architecture for Object Detection—0
Classifier-head Informed Feature Masking and Prototype-based Logit Smoothing for Out-of-Distribution Detection—0
Sneakoscope: Revisiting Unsupervised Out-of-Distribution Detection—0
Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks—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
FARE: A Deep Learning-Based Framework for Radar-based Face Recognition and Out-of-distribution Detection—0
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