In this study the application of deep learning networks in architectural design is explored via experimental plan generation. With image processing abilities of deep learning networks such as GAN (generative adversarial network), training generative models with architectural visual data is possible. One type of GANs called CycleGAN is specially chosen for the purposes of this study because of its flexibility on visual datasets and low requirement of preliminary labor. In the scope of this study, 2D plans and visuals are selected as datasets to train the CycleGAN model. Instead of training the model with only one dataset of plans and let it generate similar but novel outcomes, in this study two datasets are used to experiment on translations into plan-like images from a different dataset. For the dataset that consists of plans, Palladio’s plans are selected. Because the embedded spatial organizational data can be easily decoded and used as a training set for the CycleGAN algorithm, thanks to their potent and symmetrical representations on 2D. Second dataset is formed by Haeckel’s microorganism drawings, in order to investigate new possibilities of spatial organization when they are emerged from the visual data of organism structures. Instead of original microorganism images, Haeckel’s drawings are selected because of their idealized plan-like figures with rotational symmetry. The model was trained with these two datasets to perform image translation between them. Although the model can work both ways, this paper focused on and evaluated the translations from Haeckel’s microorganism drawings to Palladian-like plans. Eventually the model translated Haeckel’s drawings into plan-like images which shows the features of the forming patterns of Palladian plans. The outcomes can be beneficial and inspiring for the conceptual and preliminary design processes as well as studying the visual transformations between architectural and out of field visuals. This study, contributes to the field in terms of the application of AI methods -specifically GANs- in experimental plan generation tasks.