论文标题
联合网络：适合小型数据集的深度神经网络模型
Union-net: A deep neural network model adapted to small data sets
论文作者
论文摘要
在实际应用中，通常可以获得较小的数据集。目前，机器学习的大多数实际应用都使用基于大数据的经典模型来解决小型数据集的问题。但是，深度神经网络模型具有复杂的结构，巨大的模型参数和培训需要更高级的设备，这给应用程序带来了某些困难。因此，本文提出了工会卷积的概念，设计了具有浅网络结构的光线深网模型联合网络，并适应了小型数据集。该模型将卷积网络单元与相同输入的不同组合结合在一起，形成联合模块。每个联合模块等效于卷积层。 3个模块之间的串行输入和输出构成了“ 3层”神经网络。每个联合模块的输出融合并添加为最后一个卷积层的输入，以形成具有4层网络结构的复杂网络。它解决了深层网络模型网络太深并且传输路径太长的问题，这会导致基础信息传输的丢失。由于该模型的模型参数较少，并且通道较少，因此它可以更好地适应小型数据集。它解决了一个问题，即深网模型容易过度培训小型数据集。使用公共数据集CIFAR10和17Flowers进行多分类实验。实验表明，联合网络模型可以在大型数据集和小数据集的分类中表现良好。它在每日应用方案中具有很高的实际价值。该模型代码发表在https://github.com/yeaso/union-net上
In real applications, generally small data sets can be obtained. At present, most of the practical applications of machine learning use classic models based on big data to solve the problem of small data sets. However, the deep neural network model has complex structure, huge model parameters, and training requires more advanced equipment, which brings certain difficulties to the application. Therefore, this paper proposes the concept of union convolution, designing a light deep network model union-net with a shallow network structure and adapting to small data sets. This model combines convolutional network units with different combinations of the same input to form a union module. Each union module is equivalent to a convolutional layer. The serial input and output between the 3 modules constitute a "3-layer" neural network. The output of each union module is fused and added as the input of the last convolutional layer to form a complex network with a 4-layer network structure. It solves the problem that the deep network model network is too deep and the transmission path is too long, which causes the loss of the underlying information transmission. Because the model has fewer model parameters and fewer channels, it can better adapt to small data sets. It solves the problem that the deep network model is prone to overfitting in training small data sets. Use the public data sets cifar10 and 17flowers to conduct multi-classification experiments. Experiments show that the Union-net model can perform well in classification of large data sets and small data sets. It has high practical value in daily application scenarios. The model code is published at https://github.com/yeaso/union-net
