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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
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core QuantitiesCode1
Continual Learning Based on OOD Detection and Task MaskingCode1
CAN bus intrusion detection based on auxiliary classifier GAN and out-of-distribution detectionCode1
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?Code1
Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic SegmentationCode1
Contrastive Out-of-Distribution Detection for Pretrained TransformersCode1
Diffusion for Out-of-Distribution Detection on Road Scenes and BeyondCode1
CODiT: Conformal Out-of-Distribution Detection in Time-Series DataCode1
How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection?Code1
Adversarial vulnerability of powerful near out-of-distribution detectionCode1
CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say NoCode1
A Simple Fix to Mahalanobis Distance for Improving Near-OOD DetectionCode1
A framework for benchmarking class-out-of-distribution detection and its application to ImageNetCode1
AdaptiveMix: Improving GAN Training via Feature Space ShrinkageCode1
Block Selection Method for Using Feature Norm in Out-of-distribution DetectionCode1
A Theoretical Study on Solving Continual LearningCode1
Augmenting Softmax Information for Selective Classification with Out-of-Distribution DataCode1
A Hybrid Architecture for Out of Domain Intent Detection and Intent DiscoveryCode1
Deep Anomaly Detection with Outlier ExposureCode1
Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic AssemblyCode1
Background Data Resampling for Outlier-Aware ClassificationCode1
Combine and Conquer: A Meta-Analysis on Data Shift and Out-of-Distribution DetectionCode1
Balanced Energy Regularization Loss for Out-of-distribution DetectionCode1
Accuracy on In-Domain Samples Matters When Building Out-of-Domain detectors: A Reply to Marek et al. (2021)Code1
A Benchmark and Evaluation for Real-World Out-of-Distribution Detection Using Vision-Language ModelsCode1
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