This paper addresses representational bottleneck in a network and propose a set of design principles that improves model performance significantly. We argue that a representational bottleneck may happen in a network designed by a conventional design and results in degrading the model performance. To investigate the representational bottleneck, we study the matrix rank of the features generated by ten thousand random networks. We further study the entire layer's channel configuration towards designing more accurate network architectures. Based on the investigation, we propose simple yet effective design principles to mitigate the representational bottleneck. Slight changes on baseline networks by following the principle leads to achieving remarkable performance improvements on ImageNet classification. Additionally, COCO object detection results and transfer learning results on several datasets provide other backups of the link between diminishing representational bottleneck of a network and improving performance. Code and pretrained models are available at https://github.com/clovaai/rexnet.

本文提出一种轻量级模型的通道维度配置方法，该方法可以在计算成本有限的情况下实现更好的性能，并在ImageNet分类和迁移学习任务中取得了显着的表现。

重新思考通道维度以实现高效模型设计