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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 26–50 of 629 papers

TitleStatusHype
Extremely Simple Out-of-distribution Detection for Audio-visual Generalized Zero-shot Learning—0
VisTa: Visual-contextual and Text-augmented Zero-shot Object-level OOD Detection—0
Few-Shot Graph Out-of-Distribution Detection with LLMs—0
ProHOC: Probabilistic Hierarchical Out-of-Distribution Classification via Multi-Depth NetworksCode0
Leveraging Perturbation Robustness to Enhance Out-of-Distribution DetectionCode0
Benchmarking Post-Hoc Unknown-Category Detection in Food Recognition—0
OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution DetectionCode1
OODD: Test-time Out-of-Distribution Detection with Dynamic DictionaryCode1
OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy PredictionCode1
Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning PoliciesCode1
AdaSCALE: Adaptive Scaling for OOD DetectionCode0
Mitigating Hallucinations in YOLO-based Object Detection Models: A Revisit to Out-of-Distribution Detection—0
OT-DETECTOR: Delving into Optimal Transport for Zero-shot Out-of-Distribution Detection—0
Secure On-Device Video OOD Detection Without BackpropagationCode1
Structural Entropy Guided Unsupervised Graph Out-Of-Distribution DetectionCode0
HALO: Robust Out-of-Distribution Detection via Joint OptimisationCode0
DOSE3 : Diffusion-based Out-of-distribution detection on SE(3) trajectories—0
Advancing Out-of-Distribution Detection via Local NeuroplasticityCode0
Enhancing Out-of-Distribution Detection in Medical Imaging with Normalizing FlowsCode0
Controlling Neural Collapse Enhances Out-of-Distribution Detection and Transfer Learning—0
A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution DetectionCode0
Mitigating the Modality Gap: Few-Shot Out-of-Distribution Detection with Multi-modal Prototypes and Image Bias Estimation—0
A Benchmark and Evaluation for Real-World Out-of-Distribution Detection Using Vision-Language ModelsCode1
Outlier Synthesis via Hamiltonian Monte Carlo for Out-of-Distribution DetectionCode0
Score Combining for Contrastive OOD Detection—0
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