Deep learning model optimization in creative generation for new media animated ads

This study delves into the application of deep learning models based on improved Generative Adversarial Networks (GANs) and Variable Auto-Encoders (VAEs) for creative generation of animated advertisements in new media. By constructing an effective optimization model framework, the study significantly improves the visual quality, creative diversity and user engagement of advertisement images. The experimental results show that the improved GANs (V-GANs) outperform the traditional generation models, such as Vanilla GAN, VAE and CGAN, in several aspects, including visual quality, creative diversity and user engagement. The PSNR and SSIM metrics of the V-GANs reach 33.5 dB and 0.92, respectively, in generating advertisement images, which shows its detail preservation and realism presentation advantages. In addition, V-GANs also perform well in terms of creative diversity, and the ad images they generate are more innovative and unique. In the user engagement evaluation, the interaction rate of V-GANs is as high as 14.8%, indicating that their generated advertisements are more capable of attracting users' attention and engagement. The improved V-GANs model will help to promote the further development of new media ad creative generation technology and provide more powerful and intelligent solutions for brand marketing.