Title: Comparison between layer-to-layer network training and conventional network training using Deep 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 VGG16, ResNext, and DenseNet networks 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, ResNext, and DeseNet networks 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。然而，CNN的性能高度依赖于其架构与训练过程。本研究提出一种逐层训练方法，并将其与传统训练方法的性能进行对比。在逐层训练方法中，我们将早期部分网络层视为学生网络，后期网络层视为教师网络。在每个训练步骤中，我们逐步训练学生网络以学习教师网络的输出，反之亦然。我们在未使用预训练ImageNet权重的VGG16、ResNext和DenseNet网络以及常规CNN模型上评估该方法。实验表明，逐层训练方法在两种模型上均优于传统训练方法。具体而言，采用逐层训练的VGG16、ResNext、DenseNet网络及CNN模型在测试集上均取得了更高的准确率。本研究凸显了逐层训练对CNN的重要性，并表明逐层训练有望成为提升CNN准确性的有效方案。