Antagonism between Classification and Reconstruction Processes in Deep Predictive Coding Networks
Jan Rathjens, Laurenz Wiskott · 2024
Predictive coding-inspired deep networks for visual computing integrate classification and reconstruction processes in shared intermediate layers.Although synergy between these processes is commonly assumed, it has yet to be convincingly demonstrated.In this study, we utilize a purposefully designed family of autoencoder-like architectures with an added classification head to examine the consequences of combining classificationand reconstruction-driven information within the models' latent layers.Our findings underscore a significant challenge: Classification-driven information diminishes reconstruction-driven information in shared representations and vice versa.Our results challenge prevailing assumptions in predictive coding and offer guidance for future iterations of predictive coding concepts in deep networks.