A Simple Approach to Adversarial Robustness in Few-shot Image Classification-猫眼课题宝

DOI：

10.48550/arxiv.2204.05432

发表时间：

2022-04

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Akshayvarun Subramanya;H. Pirsiavash

少镜头图像分类的目标是推广到具有有限标记数据的任务，多年来已经取得了很大的进展。然而，分类器容易受到对抗性样本的影响，这就对其泛化能力提出了问题。最近的工作已经尝试将联合收割机元学习方法与对抗训练相结合，以提高少镜头分类器的鲁棒性。我们证明了一个简单的基于迁移学习的方法可以用来训练对抗性强的少数镜头分类器。我们还提出了一种新的分类任务的基础上校准的质心的少数镜头类别对基类的方法。我们表明，在新类别中，基于基本类别的标准对抗训练沿着校准的基于质心的分类器，在标准基准上的少数学习中优于或与最先进的先进方法相当。我们的方法简单，易于扩展，并且只需很少的努力就可以产生强大的少镜头分类器。代码可在此处获取：\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}

A Simple Approach to Adversarial Robustness in Few-shot Image Classification

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