To extend the proposed method for analyzing representations in earlier layers of the CNN, several strategies could be employed. First, the linking network could be adapted to connect not only the penultimate layer but also the intermediate layers of the CNN to the latent space of the pre-trained GAN, such as StyleGAN-XL. This would involve generating activation patterns from these earlier layers and creating corresponding (w, r)-pairs for training the linking network.
Second, the analysis pipeline could be modified to account for the different types of features learned at various depths of the network. For instance, earlier layers typically capture low-level features such as edges and textures, while deeper layers capture more abstract concepts. By employing techniques like feature visualization and unsupervised tracking methods, the system could systematically quantify and visualize the changes in these low-level features as the activations are perturbed.
Additionally, the few-shot image segmentation approach could be adapted to segment and analyze features at different layers, allowing for a comprehensive understanding of how features evolve from low-level to high-level representations. This multi-layer analysis could provide insights into the hierarchical nature of feature learning in CNNs, revealing how complex representations are built from simpler components.