  In recent years, deeper and wider neural networks have shown excellent
performance in computer vision tasks, while their enormous amount of parameters
results in increased computational cost and overfitting. Several methods have
been proposed to compress the size of the networks without reducing network
performance. Network pruning can reduce redundant and unnecessary parameters
from a network. Knowledge distillation can transfer the knowledge of deeper and
wider networks to smaller networks. The performance of the smaller network
obtained by these methods is bounded by the predefined network. Neural
architecture search has been proposed, which can search automatically the
architecture of the networks to break the structure limitation. Also, there is
a dynamic configuration method to train networks incrementally as sub-networks.
In this paper, we present a novel incremental training algorithm for deep
neural networks called planting. Our planting can search the optimal network
architecture with smaller number of parameters for improving the network
performance by augmenting channels incrementally to layers of the initial
networks while keeping the earlier trained parameters fixed. Also, we propose
using the knowledge distillation method for training the channels planted. By
transferring the knowledge of deeper and wider networks, we can grow the
networks effectively and efficiently. We evaluate the effectiveness of the
proposed method on different datasets such as CIFAR-10/100 and STL-10. For the
STL-10 dataset, we show that we are able to achieve comparable performance with
only 7% parameters compared to the larger network and reduce the overfitting
caused by a small amount of the data.
