Semi-supervised blockwisely architecture search for efficient lightweight generative adversarial network

Abstract In the field of computer vision, methods that use fully supervised learning and fixed deep network structures need to be improved. Currently, many studies are devoted to designing neural architecture search methods to use neural networks in a more flexible way. However, most of these methods use fully supervised learning at the cost of extraordinary GPU training time. In view of the above problems, we propose a semi-supervised generative adversarial network and search network architecture based on block structure. Use real pictures and generated pictures with corresponding real tags and pseudo tags for training, to achieve the purpose of semi-supervised learning. By setting the layer’s hyperparameters to a variable and flexible stacking block structure, network architecture search is achieved. The proposed method realizes image generation and extends to image classification. In the experimental results in Section 4, the training time is greatly reduced and the model performance is improved, which illustrates the efficiency of our method. The code can be found in https://github.com/AICV-CUMT/STASGAN .