Abstract
These days deep learning is the fastest-growing area in the field of Machine Learning. Convolutional Neural Networks are currently the main tool used for image analysis and classification purposes. Although great achievements and perspectives, deep neural networks and accompanying learning algorithms have some relevant challenges to tackle. In this paper, we have focused on the most frequently mentioned problem in the field of machine learning, that is relatively poor generalization abilities. Partial remedies for this are regularization techniques e.g. dropout, batch normalization, weight decay, transfer learning, early stopping and data augmentation. In this paper, we have focused on data augmentation. We propose to use a method based on a neural style transfer, which allows generating new unlabeled images of a high perceptual quality that combine the content of a base image with the appearance of another one. In a proposed approach, the newly created images are described with pseudo-labels, and then used as a training dataset. Real, labeled images are divided into the validation and test set. We validated the proposed method on a challenging skin lesion classification case study. Four representative neural architectures are examined. Obtained results show the strong potential of the proposed approach.
Abstract (translated)
如今，深度学习是机器学习领域发展最快的领域。卷积神经网络是目前用于图像分析和分类的主要工具。尽管取得了巨大的成就和前景，但是深层神经网络及其伴随的学习算法仍面临着一些相关的挑战。本文着重研究了机器学习领域中最常见的问题，即泛化能力相对较差的问题。部分补救措施是规范化技术，例如退出、批量规范化、重量衰减、传输学习、早期停止和数据增强。在本文中，我们主要研究数据扩充。我们建议使用一种基于神经风格传递的方法，它允许生成新的高感知质量的未标记图像，将基础图像的内容与另一个图像的外观相结合。在一个提议的方法中，新创建的图像用伪标签描述，然后作为训练数据集。真实的标记图像被分为验证和测试集。我们在一个具有挑战性的皮肤病变分类病例研究中验证了所提出的方法。研究了四种典型的神经结构。结果表明，该方法具有很强的应用潜力。
URL
https://arxiv.org/abs/1905.10974
