论文标题
拼图混合：利用显着性和本地统计数据以进行最佳混合
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
论文作者
论文摘要
尽管深度神经网络在适应训练分布方面取得了出色的性能，但学习的网络容易过度拟合，并且容易受到对抗攻击的影响。在这方面，最近提出了许多基于混合的增强方法。但是，这些方法主要集中于创建以前看不见的虚拟示例，有时可以为网络提供误导性的监督信号。为此，我们提出了拼图混合物，这是一种混合方法，用于明确利用显着信息和自然示例的基本统计数据。这导致一个有趣的优化问题在多标签目标之间交替，以最佳混合面膜和显着性打折的最佳运输目标。我们的实验表明，与CIFAR-100，Tiny-Imagenet和Imagenet数据集中的其他混合方法相比，拼图混合实现了最新的概括和对抗性鲁棒性结果。源代码可在https://github.com/snu-mllab/puzzlemix上获得。
While deep neural networks achieve great performance on fitting the training distribution, the learned networks are prone to overfitting and are susceptible to adversarial attacks. In this regard, a number of mixup based augmentation methods have been recently proposed. However, these approaches mainly focus on creating previously unseen virtual examples and can sometimes provide misleading supervisory signal to the network. To this end, we propose Puzzle Mix, a mixup method for explicitly utilizing the saliency information and the underlying statistics of the natural examples. This leads to an interesting optimization problem alternating between the multi-label objective for optimal mixing mask and saliency discounted optimal transport objective. Our experiments show Puzzle Mix achieves the state of the art generalization and the adversarial robustness results compared to other mixup methods on CIFAR-100, Tiny-ImageNet, and ImageNet datasets. The source code is available at https://github.com/snu-mllab/PuzzleMix.
