Gradual DropIn of Layers to Train Very Deep Neural Networks
This addresses a bottleneck in deep learning for researchers and practitioners by enabling training of deeper networks, though it is an incremental improvement over existing methods like dropout.
The paper tackles the problem of training very deep neural networks, which often fail to converge with conventional methods, by introducing DropIn layers that dynamically grow the network during training, enabling convergence in deep architectures like expanded LeNet, CIFAR-10, and ImageNet models.
We introduce the concept of dynamically growing a neural network during training. In particular, an untrainable deep network starts as a trainable shallow network and newly added layers are slowly, organically added during training, thereby increasing the network's depth. This is accomplished by a new layer, which we call DropIn. The DropIn layer starts by passing the output from a previous layer (effectively skipping over the newly added layers), then increasingly including units from the new layers for both feedforward and backpropagation. We show that deep networks, which are untrainable with conventional methods, will converge with DropIn layers interspersed in the architecture. In addition, we demonstrate that DropIn provides regularization during training in an analogous way as dropout. Experiments are described with the MNIST dataset and various expanded LeNet architectures, CIFAR-10 dataset with its architecture expanded from 3 to 11 layers, and on the ImageNet dataset with the AlexNet architecture expanded to 13 layers and the VGG 16-layer architecture.