Energy Correction Model for Out-of-Distribution Detection in Feature Space
Training an energy-based correction model in the feature space improves OOD detection performance.
Transformer-Based OOD Detection Method: BLOOD
Transformers process ID and OOD data differently, enabling effective OOD detection with the BLOOD method.
Few-Shot Out-of-Distribution Detection with ID-like Prompt Learning
A novel framework for few-shot OOD detection using ID-like prompts and CLIP to improve performance.
Enhancing Out-of-Distribution Detection with Multi-Comprehension Ensemble
Model ensemble diversity improves OOD detection performance.
Out-of-Distribution Detection for Breast Cancer Classification in Point-of-Care Ultrasound Imaging
Zuverlässige OOD-Erkennung ist entscheidend für sichere Klassifizierung von Brustkrebs in POCUS-Bildern.
Detecting Out-of-Distribution Data Using Maximum Entropy Coding
The paper proposes a method for detecting whether a set of test data was likely generated by a given default distribution, using maximum entropy coding and statistics of the data.
Improving Out-of-Distribution Detection through Synthetic Data Generation and Deep Metric Learning
Combining deep metric learning and synthetic data generation using diffusion models to improve out-of-distribution detection in classification models.
Identifying Out-of-Distribution Samples Using Statistical Testing Theory
The core message of this article is that the problem of efficiently detecting Out-of-Distribution (OOD) samples can be reframed as a statistical testing problem, and theoretical guarantees can be derived by leveraging the properties of the Wasserstein distance.
Tree-based Ensemble Learning for Detecting Out-of-Distribution Samples
A simple yet effective tree-based ensemble learning approach can effectively detect whether unseen samples come from a different distribution than the training data.
Comprehensive Review of Recent Advances in Out-of-Distribution Detection: Techniques, Challenges, and Future Directions
Out-of-distribution (OOD) detection is a crucial component in building reliable machine learning systems, aiming to identify test samples that are outside the training category space. This survey provides a comprehensive review of recent advances in OOD detection, focusing on the problem scenario perspective.
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