Building models to efficiently represent images is a central problem in the machine learning community. The brain and especially the visual cortex, has long find economical and robust solutions to solve such a problem. At the local scale, Sparse Coding is one of the most successful framework to model neural computation in the visual cortex. It directly derives from the efficient coding hypothesis, and could be thought as a competitive mechanism that describes visual stimulus using a limited number of neurons. At the structural scale Predictive Coding theory has been proposed to model the interconnection between cortical layers using feedforward and feedback connections.
The presentation introduces a model combining Sparse Coding and Predictive Coding in a hierarchical and convolutional architecture. Our model, called the Sparse Deep Predictive Coding (SDPC) was trained on several challenging databases including faces and natural images. The SDPC allows us to analyze the impact of recurrent processing at both neural organization level and perceptual level. At neural organization level, the feedback signal of the model accounted for a reorganization of the V1 association fields that promotes contour integration. At the higher level of perception, the SDPC exhibited significant denoising ability, highly correlated with the strength of the feedback from V2 to V1. The SDPC demonstrates that neuro-inspiration might be the right path to design more powerful and more robust computer vision algorithms.