Combining Active Learning and Data Augmentation for Image Classification
Yu Ma, Shaoxing Lu, Erya Xu, Yu Shan Tian, Lijian Zhou · 2020
To solve the problem that the data annotation in image classification task requires a lot of time and economic costs, and a large number of unlabeled images cannot be effectively utilized in reality, an image classification method combining active learning algorithm and data augmentation is proposed. First, data augmentation is performed on a small number of labeled samples, the classification model is initially trained, and then, according to the sampling strategy of active learning, the samples are selected and labeled by experts, which are the most conducive to model training from the unlabeled set. The labeled set is updated by adding the new labeled samples. The same process is performed until the requirements are met. In this paper, experiments are carried out on the digits dataset and the Cifar10 set. The results show that the image classification method proposed in this paper can effectively enhance the accuracy of image classification.