Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups

Understand

We propose a new method for creating computationally efficient and compact convolutional neural networks (CNNs) using a novel sparse connection structure that resembles a tree root.

• This allows a significant reduction in computational cost and number of parameters compared to state-of-the-art deep CNNs, without compromising accuracy, by exploiting the sparsity of inter-layer filter dependencies.
• We validate our approach by using it to train more efficient variants of state-of-the-art CNN architectures, evaluated on the CIFAR10 and ILSVRC datasets.
• Our results show similar or higher accuracy than the baseline architectures with much less computation, as measured by CPU and GPU timings.