Scaling Depth and Width in Deep Convolutional Networks for ImageNet Classification

This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How does the performance of deep convolutional neural networks scale with increasing model depth and width, as measured by top-1 and top-5 error rates on ImageNet, compared to shallower architectures. In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters. 5 claims were extracted from source literature; 5 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the performance of deep convolutional neural networks scale with increasing model depth and width, as measured by top-1 and top-5 error rates on ImageNet, compared to shallower architectures?

Autonomous literature synthesis. Automated review score: 9.0/10. Full text and citation available at Assignee Research.