论文标题
半监督学习的基于梯度的数据增强
Gradient-based Data Augmentation for Semi-Supervised Learning
论文作者
论文摘要
在半监督学习（SSL）中，一种称为一致性正则化（CR）的技术可实现高性能。已经证明，CR中使用的数据的多样性对于获得CR具有高歧视性能的模型非常重要。我们提出了一个新的数据增强（基于梯度的数据增强（GDA）），该数据是根据模型输出的后验概率分布的图像像素值梯度确定性计算的。我们旨在通过利用三种类型的GDA来确保CR的有效数据多样性。另一方面，已经证明了用于标记数据和未标记数据的混合方法在SSL中也有效。我们通过组合各种混合方法和GDA提出了一种名为MixGDA的SSL方法。评估了MixGDA实现的歧视性能针对13层CNN，该CNN被用作SSL研究的标准。结果，对于CIFAR-10（4000个标签），MixGDA的性能水平与有史以来最佳性能相同。对于SVHN（250个标签，500个标签和1000个标签）和CIFAR-100（10000标签），MixGDA可实现最先进的性能。
In semi-supervised learning (SSL), a technique called consistency regularization (CR) achieves high performance. It has been proved that the diversity of data used in CR is extremely important to obtain a model with high discrimination performance by CR. We propose a new data augmentation (Gradient-based Data Augmentation (GDA)) that is deterministically calculated from the image pixel value gradient of the posterior probability distribution that is the model output. We aim to secure effective data diversity for CR by utilizing three types of GDA. On the other hand, it has been demonstrated that the mixup method for labeled data and unlabeled data is also effective in SSL. We propose an SSL method named MixGDA by combining various mixup methods and GDA. The discrimination performance achieved by MixGDA is evaluated against the 13-layer CNN that is used as standard in SSL research. As a result, for CIFAR-10 (4000 labels), MixGDA achieves the same level of performance as the best performance ever achieved. For SVHN (250 labels, 500 labels and 1000 labels) and CIFAR-100 (10000 labels), MixGDA achieves state-of-the-art performance.
