混合分支网 (HybridBranchNet):为分支混合卷积神经网络架构的新型结构
Ebrahim Parcham1, Mansoor Fateh1
1Faculty of Computer Engineering, Shahrood University of Technology, Iran.
概括
一个新的HybridBranchNet模型在深度学习中提供了更高的准确性和效率. 这种新的缩放方法优化了网络尺寸,在速度和参数减少方面超过了EfficientNet.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 卷积神经网络 (ConvNets) 是可扩展的,但增加参数会增加复杂性.
- 为速度,大小和准确性优化网络结构至关重要.
研究的目的:
- 系统地检查分支机构网络结构以提高业绩.
- 提出一种新的缩放方法,以优化卷积网络中的深度,宽度和分辨率.
主要方法:
- 开发了一种基于分支神经网络的新扩展方法.
- 创建了一个HybridBranchNet网络家族,优化了深度,宽度和分辨率.
主要成果:
- 混合分支网3实现了83.1%的分类准确度.
- 拟议的网络在精度 (1.03%) 和速度 (39%) 中超过了EfficientNet,参数减少了13%.
- 在CIFAR-100数据集上达到92.3%,在Flowers-102数据集上达到98.8%.
结论:
- 与现有的ConvNets相比,HybridBranchNet提供了更高的准确性和效率.
- 拟议的缩放方法有效地减少了模型大小和参数数量,同时提高了性能.
- 尽管精度略低于CoAtNet等一些模型,但HybridBranchNet的参数数量减少使其适合常规系统.
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