Application of Image Recognition Based on Deep Learning in Visual Communication Design

This study aims to explore the application potential of image recognition based on deep learning in the field of visual communication design. Based on the Self-attention Generative Adversarial Network (SAGAN), this study deeply studies how deep learning technology affects creative image generation, automatic design process and design personalization through experiments and case studies. In terms of creative image generation, SAGAN model shows the potential of creating diverse images in visual communication design by learning different artistic styles and design elements. The experimental results show that the model can automatically generate design works with unique styles and characteristics, which provides designers with more creative inspiration and possibilities. In the aspect of personalized design, this study observed that the styles and features learned from SAGAN model can be flexibly applied to different design tasks to create more personalized design works. This provides a way for designers to incorporate personalized elements into their works and enhances the uniqueness of their design works. This study provides useful insights for the application of deep learning in visual communication design, and provides a new perspective for the intelligence and innovation in the design field.