A Noise Robust Batch Mode Semi-supervised and Active Learning Framework for Image Classification

Supervised learning with convolutional neural networks has made a great contribution to computer vision largely due to massive labeled samples. However, it is far from adequate available labeled samples for training in many applications. Realistically, annotation is a tedious, time consuming, and costly task while a strong need for specialty-oriented knowledge and skillful expert. Therefore, in order to take full advantage of limited resources to observably reduce the cost of annotation, we propose a noise robust batch mode semi-supervised and active learning framework which named NRMSL-BMAL. When querying labels in an iteration, firstly, a convolutional autoencoder cluster based batch mode active learning strategy is used for querying worthy samples from annotation experts with a cost. Then, a noise robust memorized self-learning is successively used for extending training samples without any annotation cost. Finally, these labeled samples are added to the training set for improving the performance of the target model. We perform a thorough experimental evaluation in image classification tasks, using datasets from different domains, including medical image, natural image, and a real-world application. Our experimental evaluation shows that NRMSL-BMAL is capable to observably reduce the annotation cost range from 44% to 95% while maintaining or even improving the performance of the target model.