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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 126–150 of 629 papers

TitleStatusHype
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core QuantitiesCode1
Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic SegmentationCode1
Likelihood Ratios for Out-of-Distribution DetectionCode1
Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized EmbeddingsCode1
Energy-based Hopfield Boosting for Out-of-Distribution DetectionCode1
Dream the Impossible: Outlier Imagination with Diffusion ModelsCode1
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary CodesCode1
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoderCode1
EAT: Towards Long-Tailed Out-of-Distribution DetectionCode1
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?Code1
A Rate-Distortion View of Uncertainty QuantificationCode1
Dissecting Out-of-Distribution Detection and Open-Set Recognition: A Critical Analysis of Methods and BenchmarksCode1
CAN bus intrusion detection based on auxiliary classifier GAN and out-of-distribution detectionCode1
Isotropy Maximization Loss and Entropic Score: Accurate, Fast, Efficient, Scalable, and Turnkey Neural Networks Out-of-Distribution Detection Based on The Principle of Maximum EntropyCode1
A Benchmark and Evaluation for Real-World Out-of-Distribution Detection Using Vision-Language ModelsCode1
Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay BufferCode1
Distribution Shifts at Scale: Out-of-distribution Detection in Earth ObservationCode1
Can multi-label classification networks know what they don’t know?Code1
Exploring the Limits of Out-of-Distribution DetectionCode1
Adversarial vulnerability of powerful near out-of-distribution detectionCode1
Multidimensional Uncertainty-Aware Evidential Neural NetworksCode1
Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning PoliciesCode1
On the Impact of Spurious Correlation for Out-of-distribution DetectionCode1
Negative Label Guided OOD Detection with Pretrained Vision-Language ModelsCode1
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed RecognitionCode1
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