Title: Comparison between layer-to-layer network training and conventional network training using Convolutional Neural Networks Abstract: Convolutional neural networks (CNNs) are widely used in various applications due to their effectiveness in extracting features from data. However, the performance of a CNN heavily depends on its architecture and training process. In this study, we propose a layer-to-layer training method and compare its performance with the conventional training method. In the layer-to-layer training approach, we treat a portion of the early layers as a student network and the later layers as a teacher network. During each training step, we incrementally train the student network to learn from the output of the teacher network, and vice versa. We evaluate this approach on a VGG16 network without pre-trained ImageNet weights and a regular CNN model. Our experiments show that the layer-to-layer training method outperforms the conventional training method for both models. Specifically, we achieve higher accuracy on the test set for the VGG16 network and the CNN model using layer-to-layer training compared to the conventional training method. Overall, our study highlights the importance of layer-wise training in CNNs and suggests that layer-to-layer training can be a promising approach for improving the accuracy of CNNs.

翻译：卷积神经网络（CNN）因其在数据特征提取方面的有效性而被广泛应用于各类任务中。然而，CNN的性能在很大程度上取决于其架构和训练过程。本研究提出一种逐层训练方法，并将其与传统训练方法进行性能比较。在逐层训练策略中，我们将网络早期部分视为学生网络，后期部分视为教师网络。在每个训练步骤中，我们逐步训练学生网络使其学习教师网络的输出，反之亦然。我们分别在未使用ImageNet预训练权重的VGG16网络和常规CNN模型上评估该方法。实验结果表明，对于这两种模型，逐层训练方法均优于传统训练方法。具体而言，采用逐层训练的VGG16网络和CNN模型在测试集上取得了比传统训练方法更高的准确率。本研究总体强调了CNN中层间训练的重要性，并表明逐层训练是提升CNN准确率的一种有前景的方法。