一个双向前神经网络架构使用分离的神经记忆普通微分方程
1College of Computer Science, Sichuan University, Chengdu 610065, P. R. China.
International journal of neural systems
|February 6, 2024
概括
双向前神经网络 (BiFNNs) 通过向前和向后路径的聚合特征来增强图像识别. 这种新的架构集成了现有的FNN,显示了与当前模型相比的显著改进.
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:
- 具有跳过连接的深度前神经网络 (FNN) 在图像识别中是至关重要的.
- 现有的架构在功能聚合和适应性方面面临限制.
研究的目的:
- 介绍一个新的双向FNN (BiFNN) 架构.
- 在深度学习中增强特征聚合和模型灵活性,用于图像识别.
主要方法:
- 开发了BiFNN,将前方FNN与非参数的后方路径集成在一起,使用离散的神经记忆普通微分方程 ([公式:见文本]-net).
- 为[公式:参见文本]-net.net提供了对收的数学证明.
- 在不同的图像识别数据集上对BiFNN进行了评估:时尚-MNIST,SVHN,CIFAR-10,CIFAR-100和Tiny-ImageNet.
主要成果:
- 与ConvMixer,ResNet,ResNeXt和Vision Transformer相比,BiFNNs表现出了显著的性能改进.
- 该架构通过接受各种FNN作为插件,以最小的参数增加来显示灵活性.
- 在Tiny-ImageNet和ImageNet-1K上微调BiFNN的性能与嵌入式模型相提并论.
结论:
- 在图像识别的深度学习架构中,BiFNNs代表了显著的进步.
- 拟议的模型提供了增强的功能聚合和适应性,优于已有的模型.
- BiFNNs为神经网络设计和图像分析的未来研究提供了一个有希望的方向.
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