Large-scale complex images contain a large number of crowded and fine-grained features that are prone to information loss during Deep Learning's (DL) downsampling process. While most recent contributions focused on discriminative tasks like small object detection, fewer papers tackled complex image processing for generative tasks. This paper presents a new pipeline for extracting and clustering significant sub-images from complex, unannotated image datasets. The pipeline is composed of three main steps: (1) identifying clusters of keypoints using K-means, (2) extracting and refining sub-image frames with a region-based expansion algorithm, and (3) classifying extracted frames. Subsequently, it enables data augmentation, simplifies domain complexity, and supports conditional training, tailoring the learning process to specific contexts, and enhancing the quality of image synthesis models. Furthermore, we analyze the structure of complex image domains by investigating intra-domain gap between extracted clusters.

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.