Efficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy
Xinyi Yu, Xiaowei Wang, Jintao Rong, Mingyang Zhang, Linlin Ou

TL;DR
This paper introduces an improved re-parameterization search space and an evolutionary NAS strategy to automatically discover better re-parameterization architectures, significantly enhancing CNN performance without relying on prior operation knowledge.
Contribution
It proposes a novel, expanded re-parameterization search space and a directional evolutionary strategy for NAS to optimize re-parameterization architectures automatically.
Findings
Improved ResNet-50 accuracy by 1.82% on ImageNet-1k.
Designed a flexible search space including more re-parameterization operations.
Achieved better results than baseline models under same training conditions.
Abstract
Structural re-parameterization (Rep) methods has achieved significant performance improvement on traditional convolutional network. Most current Rep methods rely on prior knowledge to select the reparameterization operations. However, the performance of architecture is limited by the type of operations and prior knowledge. To break this restriction, in this work, an improved re-parameterization search space is designed, which including more type of re-parameterization operations. Concretely, the performance of convolutional networks can be further improved by the search space. To effectively explore this search space, an automatic re-parameterization enhancement strategy is designed based on neural architecture search (NAS), which can search a excellent re-parameterization architecture. Besides, we visualize the output features of the architecture to analyze the reasons for the…
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Taxonomy
TopicsCancer-related molecular mechanisms research · MicroRNA in disease regulation · Advanced Neural Network Applications
Methods1x1 Convolution · Average Pooling · Batch Normalization · Bottleneck Residual Block · *Communicated@Fast*How Do I Communicate to Expedia? · Kaiming Initialization · Max Pooling · Convolution · Residual Connection · Global Average Pooling
