Extending SODE with random-network feature generation for spatial segmentation. The resulting unsupervised image segmentation system keeps working under severe noise and corruption, in the spirit of human vision and Gestalt principles: regions are grouped by the dynamics of the representation rather than by a trained decoder, so there is nothing to overfit and nothing to retrain when the input distribution changes.