On the Benefit of Synthetic Data for Company Logo Detection
Christian Eggert, Anton Winschel, Rainer W. Lienhart · 2015
In this paper we explore the benefits of synthetically generated data for the task of company logo detection with deep-learned features in the absence of a large training set. We use pre-trained deep convolutional neural networks for feature extraction and use a set of support vector machines for classifying those features. In order to generate sufficient training examples we synthesize artificial training images. Using a bootstrapping process, we iteratively add new synthesized examples from an unlabeled dataset to the training set. Using this setup we are able to obtain a performance which is close to the performance of the full training set.