تقرير
Explaining Classifiers using Adversarial Perturbations on the Perceptual Ball
| العنوان: | Explaining Classifiers using Adversarial Perturbations on the Perceptual Ball |
|---|---|
| المؤلفون: | Elliott, Andrew, Law, Stephen, Russell, Chris |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning |
| الوصف: | We present a simple regularization of adversarial perturbations based upon the perceptual loss. While the resulting perturbations remain imperceptible to the human eye, they differ from existing adversarial perturbations in that they are semi-sparse alterations that highlight objects and regions of interest while leaving the background unaltered. As a semantically meaningful adverse perturbations, it forms a bridge between counterfactual explanations and adversarial perturbations in the space of images. We evaluate our approach on several standard explainability benchmarks, namely, weak localization, insertion deletion, and the pointing game demonstrating that perceptually regularized counterfactuals are an effective explanation for image-based classifiers. Comment: CVPR 2021 |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/1912.09405Test |
| رقم الانضمام: | edsarx.1912.09405 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |