Deep Convolutional Neural Networks with Merge-and-Run Mappings
This work addresses training efficiency for deep neural networks in computer vision, offering incremental improvements over existing ResNet architectures.
The paper tackled the problem of training difficulty in deep residual networks by introducing merge-and-run blocks that assemble residual branches in parallel to improve information flow, resulting in competitive performance such as 3.57% testing error on CIFAR-10 and 19.00% on CIFAR-100.
A deep residual network, built by stacking a sequence of residual blocks, is easy to train, because identity mappings skip residual branches and thus improve information flow. To further reduce the training difficulty, we present a simple network architecture, deep merge-and-run neural networks. The novelty lies in a modularized building block, merge-and-run block, which assembles residual branches in parallel through a merge-and-run mapping: Average the inputs of these residual branches (Merge), and add the average to the output of each residual branch as the input of the subsequent residual branch (Run), respectively. We show that the merge-and-run mapping is a linear idempotent function in which the transformation matrix is idempotent, and thus improves information flow, making training easy. In comparison to residual networks, our networks enjoy compelling advantages: they contain much shorter paths, and the width, i.e., the number of channels, is increased. We evaluate the performance on the standard recognition tasks. Our approach demonstrates consistent improvements over ResNets with the comparable setup, and achieves competitive results (e.g., $3.57\%$ testing error on CIFAR-$10$, $19.00\%$ on CIFAR-$100$, $1.51\%$ on SVHN).