Propose an alternative architecture or modification to your existing sparse coding model to address the issue of poor sample quality. Explain how this addresses the limitations of the factorial prior on hidden codes.

Replace the sparse coding layer with a hierarchical representation learning module (e.g., using convolutional autoencoders). This allows for more flexible and interpretable feature extraction, and can mitigate the issue of poor sample quality by imposing a non-factorial prior on the hidden representations. However, this may come at the cost of increased computational complexity and training time.