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Out-of-Distribution Detection

161 directly classified papers

Papers per year

Papers

WeiPer: OOD Detection using Weight Perturbations of Class Projections NIPS 2024
AHA: Human-Assisted Out-of-Distribution Generalization and Detection NIPS 2024
AdaNeg: Adaptive Negative Proxy Guided OOD Detection with Vision-Language Models NIPS 2024
The Best of Both Worlds: On the Dilemma of Out-of-distribution Detection NIPS 2024
Out-of-Distribution Detection in Long-Tailed Recognition with Calibrated Outlier Class Learning AAAI 2024
SeTAR: Out-of-Distribution Detection with Selective Low-Rank Approximation NIPS 2024
TagFog: Textual Anchor Guidance and Fake Outlier Generation for Visual Out-of-Distribution Detection AAAI 2024
Learning to Shape In-distribution Feature Space for Out-of-distribution Detection NIPS 2024
Conjugated Semantic Pool Improves OOD Detection with Pre-trained Vision-Language Models NIPS 2024
Towards In-Distribution Compatible Out-of-Distribution Detection AAAI 2023
GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-Distribution Generalization Perspective ACL 2023
“Low-Resource” Text Classification: A Parameter-Free Classification Method with Compressors ACL 2023
DAMix: Exploiting Deep Autoregressive Model Zoo for Improving Lossless Compression Generalization AAAI 2023
GVdoc - Graph-based Visual DOcument Classification ACL 2023
Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detection ACL 2023
Revisit PCA-based Technique for Out-of-Distribution Detection ICCV 2023
Contrastive Novelty-Augmented Learning: Anticipating Outliers with Large Language Models ACL 2023
Probing Out-of-Distribution Robustness of Language Models with Parameter-Efficient Transfer Learning ACL 2023
Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data NIPS 2023
Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization NIPS 2023
On the Powerfulness of Textual Outlier Exposure for Visual OoD Detection NIPS 2023
Window-Based Distribution Shift Detection for Deep Neural Networks NIPS 2023
On the Importance of Feature Separability in Predicting Out-Of-Distribution Error NIPS 2023
Key Feature Replacement of In-Distribution Samples for Out-of-Distribution Detection AAAI 2023
GradOrth: A Simple yet Efficient Out-of-Distribution Detection with Orthogonal Projection of Gradients NIPS 2023