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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 51–75 of 629 papers

TitleStatusHype
Hypercone Assisted Contour Generation for Out-of-Distribution Detection—0
A Closer Look at the Learnability of Out-of-Distribution (OOD) Detection—0
FARE: A Deep Learning-Based Framework for Radar-based Face Recognition and Out-of-distribution Detection—0
DisCoPatch: Taming Adversarially-driven Batch Statistics for Improved Out-of-Distribution Detection—0
ARES: Auxiliary Range Expansion for Outlier Synthesis—0
Harnessing Large Language and Vision-Language Models for Robust Out-of-Distribution Detection—0
Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks—0
Multi-layer Radial Basis Function Networks for Out-of-distribution Detection—0
Dual Energy-Based Model with Open-World Uncertainty Estimation for Out-of-distribution Detection—0
Overcoming Shortcut Problem in VLM for Robust Out-of-Distribution DetectionCode0
Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric EnsemblesCode0
Out-of-Distribution Detection with Prototypical Outlier ProxyCode0
Distribution Shifts at Scale: Out-of-distribution Detection in Earth ObservationCode1
Boosting LLM-based Relevance Modeling with Distribution-Aware Robust Learning—0
ITP: Instance-Aware Test Pruning for Out-of-Distribution DetectionCode0
Mining In-distribution Attributes in Outliers for Out-of-distribution DetectionCode0
EDGE: Unknown-aware Multi-label Learning by Energy Distribution Gap Expansion—0
Taylor Outlier ExposureCode0
Out-of-Distribution Detection with Overlap Index—0
Revisiting Energy-Based Model for Out-of-Distribution DetectionCode0
Learning Structured Representations with Hyperbolic EmbeddingsCode1
NCDD: Nearest Centroid Distance Deficit for Out-Of-Distribution Detection in Gastrointestinal VisionCode0
Enhancing Few-Shot Out-of-Distribution Detection with Gradient Aligned Context OptimizationCode0
Out-Of-Distribution Detection with Diversification (Provably)Code0
Non-Linear Outlier Synthesis for Out-of-Distribution DetectionCode0
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