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- W3196993835 abstract "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: this https URL." @default.
- W3196993835 created "2021-09-13" @default.
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- W3196993835 date "2021-08-31" @default.
- W3196993835 modified "2023-09-23" @default.
- W3196993835 title "Semi-supervised Image Classification with Grad-CAM Consistency." @default.
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