Self-supervised learning is popular method because of its ability to learn
features in images without using its labels and is able to overcome limited
labeled datasets used in supervised learning. Self-supervised learning works by
using a pretext task which will be trained on the model before being applied to
a specific task. There are some examples of pretext tasks used in
self-supervised learning in the field of image recognition, namely rotation
prediction, solving jigsaw puzzles, and predicting relative positions on image.
Previous studies have only used one type of transformation as a pretext task.
This raises the question of how it affects if more than one pretext task is
used and to use a gating network to combine all pretext tasks. Therefore, we
propose the Gated Self-Supervised Learning method to improve image
classification which use more than one transformation as pretext task and uses
the Mixture of Expert architecture as a gating network in combining each
pretext task so that the model automatically can study and focus more on the
most useful augmentations for classification. We test performance of the
proposed method in several scenarios, namely CIFAR imbalance dataset
classification, adversarial perturbations, Tiny-Imagenet dataset
classification, and semi-supervised learning. Moreover, there are Grad-CAM and
T-SNE analysis that are used to see the proposed method for identifying
important features that influence image classification and representing data
for each class and separating different classes properly. Our code is in
this https URL

自我监督学习方法可以通过使用多个预处理任务和混合专家架构作为门控网络，来改进图像分类，使模型能够自动学习和更关注对分类最有用的增强方法。在几个场景下进行测试，并使用 Grad-CAM 和 T-SNE 分析来识别影响图像分类的重要特征并适当地区分不同的类别。