Gradual DropIn of Layers to Train Very Deep Neural Networks

TL;DR

This paper presents DropIn, a novel layer that gradually adds new layers to deep neural networks during training, enabling convergence of very deep architectures and providing regularization.

Contribution

Introduction of DropIn layers that dynamically grow neural networks during training, improving trainability and regularization of very deep models.

Findings

Deep networks become trainable with DropIn layers.

DropIn acts as a regularizer similar to dropout.

Successful experiments on MNIST, CIFAR-10, and ImageNet datasets.

Abstract

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.