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(CNNs)由于能够有效地从数据中提取特征而被广泛应用于各种应用中,但CNN的性能主要取决于其架构和训练过程。本研究提出了一种层间训练方法,并将其表现与传统训练方法进行比较。在层间训练方法中,我们将前面的一部分层视为学生网络,后面的一部分层视为教师网络。在每个训练步骤中,我们逐步训练学生网络从教师网络的输出中学习,反之亦然。我们在一个没有经过ImageNet预训练权重的VGG16网络和一个常规CNN模型上评估了这种方法。实验结果表明,层间训练方法在两个模型中的表现均优于传统训练方法。具体来说,使用层间训练法的VGG16网络和CNN模型在测试集上的准确性均高于传统训练方法。总体而言,我们的研究强调了CNN中层次训练的重要性,并建议层间训练可以是提高CNN准确性的一种有前途的方法。