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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 1–25 of 629 papers

TitleStatusHype
Logits-Based FinetuningCode2
Extremely Simple Multimodal Outlier Synthesis for Out-of-Distribution Detection and SegmentationCode2
DPU: Dynamic Prototype Updating for Multimodal Out-of-Distribution DetectionCode2
Recent Advances in OOD Detection: Problems and ApproachesCode2
GalLoP: Learning Global and Local Prompts for Vision-Language ModelsCode2
Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A BenchmarkCode2
Learning Transferable Negative Prompts for Out-of-Distribution DetectionCode2
Rethinking Test-time Likelihood: The Likelihood Path Principle and Its Application to OOD DetectionCode2
MOODv2: Masked Image Modeling for Out-of-Distribution DetectionCode2
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human FeedbackCode2
VOS: Learning What You Don't Know by Virtual Outlier SynthesisCode2
OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution DetectionCode1
OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy PredictionCode1
OODD: Test-time Out-of-Distribution Detection with Dynamic DictionaryCode1
Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning PoliciesCode1
Secure On-Device Video OOD Detection Without BackpropagationCode1
A Benchmark and Evaluation for Real-World Out-of-Distribution Detection Using Vision-Language ModelsCode1
Distribution Shifts at Scale: Out-of-distribution Detection in Earth ObservationCode1
Learning Structured Representations with Hyperbolic EmbeddingsCode1
Generalize or Detect? Towards Robust Semantic Segmentation Under Multiple Distribution ShiftsCode1
Self-Calibrated Tuning of Vision-Language Models for Out-of-Distribution DetectionCode1
Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized EmbeddingsCode1
The Best of Both Worlds: On the Dilemma of Out-of-distribution DetectionCode1
Conjugated Semantic Pool Improves OOD Detection with Pre-trained Vision-Language ModelsCode1
Uncertainty Estimation by Density Aware Evidential Deep LearningCode1
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