IC-Network: Efficient Structure for Convolutional Neural Networks
This work addresses the need for efficient network structures to improve training speed and generalization in deep learning, representing an incremental advancement.
The authors tackled the problem of designing more effective basic units for convolutional neural networks by introducing an Inter-layer Collision (IC) structure inspired by physics, which reduced the top-1 error of ResNet-50 on ImageNet from 22.85% to 21.49%.
Neural networks have been widely used, and most networks achieve excellent performance by stacking certain types of basic units. Compared to increasing the depth and width of the network, designing more effective basic units has become an important research topic. Inspired by the elastic collision model in physics, we present a universal structure that could be integrated into the existing network structures to speed up the training process and increase their generalization abilities. We term this structure the "Inter-layer Collision" (IC) structure. We built two kinds of basic computational units (IC layer and IC block) that compose the convolutional neural networks (CNNs) by combining the IC structure with the convolution operation. Compared to traditional convolutions, both of the proposed computational units have a stronger non-linear representation ability and can filter features useful for a given task. Using these computational units to build networks, we bring significant improvements in performance for existing state-of-the-art CNNs. On the imagenet experiment, we integrate the IC block into ResNet-50 and reduce the top-1 error from 22.85% to 21.49%, which also exceeds the top-1 error of ResNet-100 (21.75%).