Exploitation of Noisy Automatic Data Annotation and Its Application to Hand Posture Classification

The success of deep learning in recent years relies on the availability of large amounts of accurately annotated training data. In this work, we investigate a technique for utilizing automatically annotated data in classification problems. Using a small number of manually annotated samples, and a large set of data that feature automatically created, noisy labels, our approach trains a Convolutional Neural Network (CNN) in an iterative manner. The automatic annotations are combined with the predictions of the network in order to gradually expand the training set. In order to evaluate the performance of the proposed approach, we apply it to the problem of hand posture recognition from RGB images. We compare the results of training a CNN classifier with and without the use of our technique. Our method yields a significant increase in average classification accuracy, and also decreases the deviation in class accuracies, thus indicating the validity and the usefulness of the proposed approach.