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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 1–50 of 629 papers

TitleStatusHype
MOODv2: Masked Image Modeling for Out-of-Distribution DetectionCode2
DPU: Dynamic Prototype Updating for Multimodal Out-of-Distribution DetectionCode2
Recent Advances in OOD Detection: Problems and ApproachesCode2
Learning Transferable Negative Prompts for Out-of-Distribution DetectionCode2
Rethinking Test-time Likelihood: The Likelihood Path Principle and Its Application to OOD DetectionCode2
Logits-Based FinetuningCode2
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human FeedbackCode2
Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A BenchmarkCode2
GalLoP: Learning Global and Local Prompts for Vision-Language ModelsCode2
Extremely Simple Multimodal Outlier Synthesis for Out-of-Distribution Detection and SegmentationCode2
VOS: Learning What You Don't Know by Virtual Outlier SynthesisCode2
Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning PoliciesCode1
Energy-based Out-of-Distribution Detection for Graph Neural NetworksCode1
Exploring the Limits of Out-of-Distribution DetectionCode1
EAT: Towards Long-Tailed Out-of-Distribution DetectionCode1
Can multi-label classification networks know what they don't know?Code1
Energy-based Hopfield Boosting for Out-of-Distribution DetectionCode1
Feature Space Singularity for Out-of-Distribution DetectionCode1
Detecting Out-of-Distribution Examples with In-distribution Examples and Gram MatricesCode1
Dissecting Out-of-Distribution Detection and Open-Set Recognition: A Critical Analysis of Methods and BenchmarksCode1
Agree to Disagree: Diversity through Disagreement for Better TransferabilityCode1
Diffusion for Out-of-Distribution Detection on Road Scenes and BeyondCode1
A Multi-Head Model for Continual Learning via Out-of-Distribution ReplayCode1
CODiT: Conformal Out-of-Distribution Detection in Time-Series DataCode1
Can multi-label classification networks know what they don’t know?Code1
Dream the Impossible: Outlier Imagination with Diffusion ModelsCode1
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?Code1
CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say NoCode1
How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection?Code1
Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic SegmentationCode1
Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic AssemblyCode1
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
Contrastive Out-of-Distribution Detection for Pretrained TransformersCode1
Beyond AUROC & co. for evaluating out-of-distribution detection performanceCode1
Adversarial vulnerability of powerful near out-of-distribution detectionCode1
Continual Learning Based on OOD Detection and Task MaskingCode1
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
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core QuantitiesCode1
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
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution DataCode1
Detection of out-of-distribution samples using binary neuron activation patternsCode1
Background Data Resampling for Outlier-Aware ClassificationCode1
Deep Anomaly Detection with Outlier ExposureCode1
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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