Enhancing Complex Image Synthesis with Conditional Generative Models and Rule Extraction

Generative Adversarial Networks (GANs) have shown potential for generating images, but have limitations when applied to complex datasets. To address these limitations, class-conditional training is employed, as it performs better and maintains a high level of semantic diversity. In this work, we propose a new method for training generative models on complex images by extracting rules defining the relationships between objects in the image, cropping significant sub-regions based on these rules, and training the models in a conditional setting using the extracted rules as labels. The proposed approach is evaluated and the results demonstrate its effectiveness by increasing the training dataset size, and then feeding it to conditional training. As a result, synthesized samples maintain asset fine-grained details and the visibility of small instances.