基于NSGA-Ⅱ的自适应多尺度特征通道分组优化算法

Adaptive multi-scale feature channel grouping optimization algorithm based on NSGA‑Ⅱ

针对轻量型卷积神经网络(LCNN)的精确度和复杂度均衡优化问题,提出基于快速非支配排序遗传算法(NSGA-Ⅱ)的自适应多尺度特征通道分组优化算法对LCNN特征通道分组结构进行优化。首先,将LCNN中的特征融合层结构的复杂度最小化和精确度最大化作为两个优化目标,进行双目标函数建模及理论分析;然后,设计基于NSGA-Ⅱ的LCNN结构优化框架,并在原始LCNN结构的深度卷积层之上增加基于NSGA-Ⅱ的自适应分组层,构建基于NSGA-Ⅱ的自适应多尺度的特征融合网络NSGA2-AMFFNetwork。在图像分类数据集上的实验结果显示,与手工设计的网络结构M_blockNet_v1相比,NSGA2-AMFFNetwork的平均精确度提升了1.2202个百分点,运行时间降低了41.07%。这表明所提优化算法能较好平衡LCNN的复杂度和精确度,同时还可为领域知识不足的普通用户提供更多性能表现均衡的网络结构选择方案。

Aiming at the balance optimization problem of Lightweight Convolutional Neural Network(LCNN)in accuracy and complexity,an adaptive multi-scale feature channel grouping optimization algorithm based on fast Nondominated Sorting Genetic Algorithm(NSGA-Ⅱ)was proposed to optimize the feature channel grouping structure of LCNN.Firstly,the complexity minimization and accuracy maximization of the feature fusion layer structure in LCNN were regarded as two optimization objectives,and the dual-objective function modeling and theoretical analysis were carried out.Then,a LCNN structure optimization framework based on NSGA-Ⅱwas designed,and an adaptive grouping layer based on NSGA-Ⅱwas added to deep convolution layer in original LCNN structure,thus constructing an Adaptive Multi-scale Feature Fusion Network based on NSGA2(NSGA2-AMFFNetwork).Experimental results on image classification datasets show that compared with the manually designed network structure M_blockNet_v1,NSGA2-AMFFNetwork has the average accuracy improved by 1.2202 percentage points,and the running time decreased by 41.07%.This above indicates that the proposed optimization algorithm can balance the complexity and accuracy of LCNN,and also provide more options for network structure with balanced performance for ordinary users who lack domain knowledge.