CrescendoNet: A Simple Deep Convolutional Neural Network with Ensemble Behavior
arXiv:1710.11176
Abstract
We introduce a new deep convolutional neural network, CrescendoNet, by stacking simple building blocks without residual connections. Each Crescendo block contains independent convolution paths with increased depths. The numbers of convolution layers and parameters are only increased linearly in Crescendo blocks. In experiments, CrescendoNet with only 15 layers outperforms almost all networks without residual connections on benchmark datasets, CIFAR10, CIFAR100, and SVHN. Given sufficient amount of data as in SVHN dataset, CrescendoNet with 15 layers and 4.1M parameters can match the performance of DenseNet-BC with 250 layers and 15.3M parameters. CrescendoNet provides a new way to construct high performance deep convolutional neural networks without residual connections. Moreover, through investigating the behavior and performance of subnetworks in CrescendoNet, we note that the high performance of CrescendoNet may come from its implicit ensemble behavior, which differs from the FractalNet that is also a deep convolutional neural network without residual connections. Furthermore, the independence between paths in CrescendoNet allows us to introduce a new path-wise training procedure, which can reduce the memory needed for training.
References in corpus (11)
- Adam: A Method for Stochastic Optimization
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
- Improving neural networks by preventing co-adaptation of feature detectors
- Deep Residual Learning for Image Recognition
- Striving for Simplicity: The All Convolutional Net
- Densely Connected Convolutional Networks
- Wide Residual Networks
- Residual Networks Behave Like Ensembles of Relatively Shallow Networks
- Aggregated Residual Transformations for Deep Neural Networks
- Xception: Deep Learning with Depthwise Separable Convolutions