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Semi-supervised Image Classification with Grad-CAM Consistency

2021-08-31 08:26:35
Juyong Lee, Seunghyuk Cho
arXiv_CV
       
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.

Abstract (translated)

URL

https://arxiv.org/abs/2108.13673

PDF

https://arxiv.org/pdf/2108.13673.pdf

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