Towards Deep Compositional Networks

Understand

Hierarchical feature learning based on convolutional neural networks (CNN) has recently shown significant potential in various computer vision tasks.

• While allowing high-quality discriminative feature learning, the downside of CNNs is the lack of explicit structure in features, which often leads to overfitting, absence of reconstruction from partial observations and limited generative abilities.
• Explicit structure is inherent in hierarchical compositional models, however, these lack the ability to optimize a well-defined cost function.
• We propose a novel analytic model of a basic unit in a layered hierarchical model with both explicit compositional structure and a well-defined discriminative cost function.