Improving Semi-Supervised Image Classification by Assigning Different Weights to Correctly and Incorrectly Classified Samples

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Abstract
Semi-supervised deep learning, a model that aims to effectively use unlabeled data to help learn sample features from labeled data, is a recent hot topic. To effectively use unlabeled data, a new semi-supervised learning model based on a consistency strategy is proposed. In the supervised part with labeled samples, the image generation model first generates some artificial images to complement the limited number of labeled samples. Secondly, the sample label mapping, as the “benchmark”, is compared to the corresponding sample features in the network as an additional loss to complement the original supervisory loss, aiming to better correct the model parameters. Finally, the original supervised loss is changed so that the network parameters are determined by the characteristics of each correctly classified sample. In the unsupervised part, the actual unsupervised loss is altered so that the model does not “treat all samples equally” and can focus more on the characteristics of misclassified samples. A total of 40 labeled samples from the CIFAR-10 and SVHN datasets were used to train the semi-supervised model achieving accuracies of 93.25% and 96.83%, respectively, demonstrating the effectiveness of the proposed semi-supervised model.

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