Deep Convolutional Neural Network Design Patterns
This work addresses the challenge for inexperienced practitioners in selecting neural network architectures, though it appears incremental as it builds on existing research.
The paper tackles the problem of overwhelming architecture choices in deep learning by mining research to discover design principles, and introduces new architectures like FractalNet, Stagewise Boosting Networks, and Taylor Series Networks.
Recent research in the deep learning field has produced a plethora of new architectures. At the same time, a growing number of groups are applying deep learning to new applications. Some of these groups are likely to be composed of inexperienced deep learning practitioners who are baffled by the dizzying array of architecture choices and therefore opt to use an older architecture (i.e., Alexnet). Here we attempt to bridge this gap by mining the collective knowledge contained in recent deep learning research to discover underlying principles for designing neural network architectures. In addition, we describe several architectural innovations, including Fractal of FractalNet network, Stagewise Boosting Networks, and Taylor Series Networks (our Caffe code and prototxt files is available at https://github.com/iPhysicist/CNNDesignPatterns). We hope others are inspired to build on our preliminary work.