تقرير
A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?
| العنوان: | A noisy elephant in the room: Is your out-of-distribution detector robust to label noise? |
|---|---|
| المؤلفون: | Humblot-Renaux, Galadrielle, Escalera, Sergio, Moeslund, Thomas B. |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| الوصف: | The ability to detect unfamiliar or unexpected images is essential for safe deployment of computer vision systems. In the context of classification, the task of detecting images outside of a model's training domain is known as out-of-distribution (OOD) detection. While there has been a growing research interest in developing post-hoc OOD detection methods, there has been comparably little discussion around how these methods perform when the underlying classifier is not trained on a clean, carefully curated dataset. In this work, we take a closer look at 20 state-of-the-art OOD detection methods in the (more realistic) scenario where the labels used to train the underlying classifier are unreliable (e.g. crowd-sourced or web-scraped labels). Extensive experiments across different datasets, noise types & levels, architectures and checkpointing strategies provide insights into the effect of class label noise on OOD detection, and show that poor separation between incorrectly classified ID samples vs. OOD samples is an overlooked yet important limitation of existing methods. Code: https://github.com/glhr/ood-labelnoise Comment: Accepted at CVPR 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2404.01775 |
| رقم الأكسشن: | edsarx.2404.01775 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |