查看文章

Sufficient training data normally is required to train deeply learned models. However, due to the expensive manual process for a labeling large number of images ( i.e. , annotation), the amount of available training data ( i.e. , real data) is always limited. To produce more data for training a deep network, generative adversarial network can be used to generate artificial sample data ( i.e. , generated data). However, the generated data usually does not have annotation labels. To solve this problem, in this paper, we propose a virtual label called Multi-pseudo Regularized Label (MpRL) and assign it to the generated data. With MpRL, the generated data will be used as the supplementary of real training data to train a deep neural network in a semi-supervised learning fashion. To build the corresponding relationship between the real data and generated data, MpRL assigns each generated data a proper virtual label …