论文标题
几次图像分类中的对抗鲁棒性的简单方法
A Simple Approach to Adversarial Robustness in Few-shot Image Classification
论文作者
论文摘要
多年来，几年的目标是概括到具有有限的标签数据的任务的几乎没有图像分类，多年来取得了长足的进步。但是，分类器容易受到对抗示例的影响，提出了有关其概括能力的问题。最近的作品试图将元学习方法与对抗性培训相结合，以提高少量分类器的鲁棒性。我们表明，一种简单的基于转移学习的方法可用于训练对抗性稳定的几个射击分类器。我们还提出了一种基于校准基本类别的几类类别的质心的新型分类任务的方法。我们表明，基本类别上的标准对抗培训以及新型类别中的基于质心的分类器，表现优于或与标准基准的最先进的高级方法相比，用于几次学习。我们的方法简单，易于缩放，而付出的努力很少会导致稳健的几弹性分类器。代码可在此处找到：\ url {https://github.com/ucdvision/simple_few_shot.git}
Few-shot image classification, where the goal is to generalize to tasks with limited labeled data, has seen great progress over the years. However, the classifiers are vulnerable to adversarial examples, posing a question regarding their generalization capabilities. Recent works have tried to combine meta-learning approaches with adversarial training to improve the robustness of few-shot classifiers. We show that a simple transfer-learning based approach can be used to train adversarially robust few-shot classifiers. We also present a method for novel classification task based on calibrating the centroid of the few-shot category towards the base classes. We show that standard adversarial training on base categories along with calibrated centroid-based classifier in the novel categories, outperforms or is on-par with state-of-the-art advanced methods on standard benchmarks for few-shot learning. Our method is simple, easy to scale, and with little effort can lead to robust few-shot classifiers. Code is available here: \url{https://github.com/UCDvision/Simple_few_shot.git}
