Automatic Data Augmentation from Massive Web Images for Deep Visual Recognition

Abstract

Large-scale image datasets and deep convolutional neural networks (DCNNs) are the two primary driving forces for the rapid progress in generic object recognition tasks in recent years. While lots of network architectures have been continuously designed to pursue lower error rates, few efforts are devoted to enlarging existing datasets due to high labeling costs and unfair comparison issues. In this article, we aim to achieve lower error rates by augmenting existing datasets in an automatic manner. Our method leverages both the web and DCNN, where the web provides massive images with rich contextual information, and DCNN replaces humans to automatically label images under the guidance of web contextual information. Experiments show that our method can automatically scale up existing datasets significantly from billions of web pages with high accuracy. The performance on object recognition tasks and transfer learning tasks have been significantly improved by using the automatically augmented datasets, which demonstrates that more supervisory information has been automatically gathered from the web. Both the dataset and models trained on the dataset have been made publicly available.