De-IReps: Searching for improved Re-parameterizing Architecture based on Differentiable Evolution Strategy

In recent years, neural architecture search (NAS) has shown great competitiveness in many fields and re-parameterization techniques have started to appear in the field of architectural search. However, most edge devices do not adapt well to networks, especially the multi-branch structure, which is searched by NAS. Therefore, in this work we design a search space that covers almost all re-parameterization operations. In this search space, multiple-path networks can be unconditionally re-parameterized into single-path networks. Thus, enhancing the usefulness of traditional nas. Meanwhile we summarize the characteristics of the re-parameterization search space and propose a differentiable evolutionary strategy (DES) to explore the re-parameterization search space. We visualize the features of the searched architecture and give our explanation for the appearance of this architecture. In this work, we can achieve efficient search and find better network structures. Respectively, we completed the architecture search on CIFAR-10 with the test accuracy of 96.64% (IrepResNet-18) and 95.65% (IrepVGG-16) and on ImageNet with the test accuracy of 77.92% (Irep-ResNet-50).