With a lower memory footprint, our method allows training with larger batches in each iteration and better utilization of available GPU resources, making it more practical and economical to deploy and train very deep architectures in production environments.
training with fewer features per layer leads to much lower accuracy, while the naive approximation baseline simply fails, highlighting the importance of preventing accumulation of errors across layers using our approach.