Coupled Ensembles of Neural Networks
This work addresses efficiency and performance in deep learning models for computer vision tasks, but it is incremental as it builds on existing state-of-the-art architectures.
The paper tackles the problem of improving deep convolutional network performance or reducing parameters by proposing a branched architecture called 'coupled ensembles', which reconfigures parameters into parallel branches and adds a tighter coupling layer. The result includes error rates of 2.92% on CIFAR-10 and 15.68% on CIFAR-100 with 25M parameters, outperforming baseline DenseNet-BC.
We investigate in this paper the architecture of deep convolutional networks. Building on existing state of the art models, we propose a reconfiguration of the model parameters into several parallel branches at the global network level, with each branch being a standalone CNN. We show that this arrangement is an efficient way to significantly reduce the number of parameters without losing performance or to significantly improve the performance with the same level of performance. The use of branches brings an additional form of regularization. In addition to the split into parallel branches, we propose a tighter coupling of these branches by placing the "fuse (averaging) layer" before the Log-Likelihood and SoftMax layers during training. This gives another significant performance improvement, the tighter coupling favouring the learning of better representations, even at the level of the individual branches. We refer to this branched architecture as "coupled ensembles". The approach is very generic and can be applied with almost any DCNN architecture. With coupled ensembles of DenseNet-BC and parameter budget of 25M, we obtain error rates of 2.92%, 15.68% and 1.50% respectively on CIFAR-10, CIFAR-100 and SVHN tasks. For the same budget, DenseNet-BC has error rate of 3.46%, 17.18%, and 1.8% respectively. With ensembles of coupled ensembles, of DenseNet-BC networks, with 50M total parameters, we obtain error rates of 2.72%, 15.13% and 1.42% respectively on these tasks.