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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 451–475 of 629 papers

TitleStatusHype
Understanding the Role of Self-Supervised Learning in Out-of-Distribution Detection Task—0
Distributionally Robust Recurrent Decoders with Random Network Distillation—0
Generalized Out-of-Distribution Detection: A SurveyCode1
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution DetectionCode1
Natural Attribute-based Shift Detection—0
Identifying Incorrect Classifications with Balanced UncertaintyCode0
Well-classified Examples are Underestimated in Classification with Deep Neural NetworksCode1
Gated Information Bottleneck for Generalization in Sequential EnvironmentsCode0
Meta Learning Low Rank Covariance Factors for Energy-Based Deterministic Uncertainty—0
On out-of-distribution detection with Bayesian neural networksCode0
Out-of-Distribution Detection for Medical Applications: Guidelines for Practical EvaluationCode0
Intra-class Mixup for Out-of-Distribution Detection—0
MOG: Molecular Out-of-distribution Generation with Energy-based Models—0
FROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection—0
Revisiting flow generative models for Out-of-distribution detection—0
Exploring Covariate and Concept Shift for Detection and Confidence Calibration of Out-of-Distribution Data—0
GRODIN: Improved Large-Scale Out-of-Domain detection via Back-propagation—0
Decomposing Texture and Semantics for Out-of-distribution Detection—0
Revisiting Out-of-Distribution Detection: A Simple Baseline is Surprisingly Effective—0
Efficient Out-of-Distribution Detection via CVAE data Generation—0
Towards Unknown-aware Learning with Virtual Outlier Synthesis—0
No Shifted Augmentations (NSA): strong baselines for self-supervised Anomaly Detection—0
Towards Unknown-aware Deep Q-Learning—0
Sneakoscope: Revisiting Unsupervised Out-of-Distribution Detection—0
Meta Learning Low Rank Covariance Factors for Energy Based Deterministic Uncertainty—0
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