  Consistency training, which exploits both supervised and unsupervised
learning with different augmentations on image, is an effective method of
utilizing unlabeled data in semi-supervised learning (SSL) manner. Here, we
present another version of the method with Grad-CAM consistency loss, so it can
be utilized in training model with better generalization and adjustability. We
show that our method improved the baseline ResNet model with at most 1.44 % and
0.31 $\pm$ 0.59 %p accuracy improvement on average with CIFAR-10 dataset. We
conducted ablation study comparing to using only psuedo-label for consistency
training. Also, we argue that our method can adjust in different environments
when targeted to different units in the model. The code is available:
https://github.com/gimme1dollar/gradcam-consistency-semi-sup.
