Intelligent Generation Design of Brand Logo Based on Generative Adversarial Network (GAN)

Brand logo is the core carrier of corporate image, and its design quality directly affects brand communication and market competitiveness. The traditional logo design process is complex and relies on the experience of designers, which is difficult to meet the diverse and efficient market needs. To this end, this paper introduces a brand logo intelligent generation method based on Generative Adversarial Networks (GAN), and optimizes the generator structure by combining Channel and Spatial Attention Mechanism (CBAM), which improves the semantic expression ability and visual effect of logo generation. The experimental research first starts with the construction and annotation of a data platform to collect and organize diversified brand logo data, and then generates an improved design of the adversarial network, which enhances the feature extraction capabilities of the generator by introducing the CBAM mechanism. Experimental results show that as training proceeds, the accuracy of the generator and discriminator gradually increases, and the accuracy of the generator increases from 0.45 to 0.67. The quality of generated images and sample diversity are significantly improved.