Computerized Logo Synthesis with Wavelets-Enhanced Adversarial Learning

Longchun Mao, Jinghua Wang, Jianmin Jiang

Abstract: While logo design requires creative thoughts from artistic side, computerized logo synthesis could provide significant assistance in terms of workload reduction and productivity improvements. By applying wavelet transform to decompose the input logo images into four frequency bands, we introduce two new regularization terms into the GAN-based adversarial learning towards improved logo synthesis. As the LL band of images preserve the primary content information, we apply clustering to these LL bands to generate supervisory labels to regulate the logo generation and hence the logo synthesis can be predominated by a label-guided theme. To create varieties and diversities for the synthesized logos, we further establish a second regularization term out of the HH-band and enable the learning process to simulate the creativity illustrated by logo designers. Extensive experiments are carried out and, compared with the existing state of the arts, the results show that our proposed achieves overwhelmingly better performances in terms of the inception scores.