Dairy Goat Image Generation Based on Improved-Self-Attention Generative Adversarial Networks

Abstract

The lack of long-range dependence in convolutional neural networks causes weaker performance in generative adversarial networks(GANs) with regard to generating image details. The self-attention generative adversarial network(SAGAN) use the self-attention mechanism to calculate the correlation coefficient between feature vectors, which improves the global coherence of the network. In this paper, we put forward an improved-self-attention GANs(Improved-SAGAN) to improve the method for calculating correlation in the SAGAN. We can better measure the correlation between features by normalizing the feature vectors to eliminate as many errors caused by noise as possible. As the network learns the global information by calculating the correlation coefficient between all features, it can make up for the defects of local receptive field in the convolution network. We replace the conventional one-hot label with multi-label to obtain more supervised information for generative adversarial networks. We generate dairy goat images based on auxiliary condition generative adversarial network(ACGAN) incorporating the normalized self-attention mechanism and prove that images generated under multi-label are of higher quality than images generated under one-hot label. The generative results of different networks on the public dataset are compared by the inception score and FID evaluation algorithms, and we propose a new evaluation algorithm called SSIM-Mean to measure the quality of generated dairy goat images to further verify the effectiveness of the improved-self-attention GANs.