  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.
