Content and image generation consist in creating or generating data from
noisy information by extracting specific features such as texture, edges, and
other thin image structures. We are interested here in generative models, and
two main problems are addressed. Firstly, the improvement
PDE-CNNs, a variant of PDE-based Group Convolutional Neural Networks, offer fewer parameters, better performance, and data efficiency compared to CNNs, while utilizing semifield-valued signals for geometric interpretability.