  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.

Machine Learning;Computer Vision and Pattern Recognition;Machine Learning