Puppet-CNN: Input-Adaptive Convolutional Neural Networks with Model Compression using Ordinary Differential Equation
This addresses efficiency and adaptability issues for deep learning practitioners, though it is incremental as it builds on existing CNN and ODE methods.
The paper tackles the problem of large parameter sizes and fixed structures in CNNs by proposing Puppet-CNN, a framework that adapts network depth and kernels based on input complexity using an ODE module, reducing model size by over 10 times while improving performance and efficiency.
Convolutional Neural Network (CNN) has been applied to more and more scenarios due to its excellent performance in many machine learning tasks, especially with deep and complex structures. However, as the network goes deeper, more parameters need to be stored and optimized. Besides, almost all common CNN models adopt "train-and-use" strategy where the structure is pre-defined and the kernel parameters are fixed after the training with the same structure and set of parameters used for all data without considering the content complexity. In this paper, we propose a new CNN framework, named as $\textit{Puppet-CNN}$, which contains two modules: a $\textit{puppet module}$ and a $\textit{puppeteer module}$. The puppet module is a CNN model used to actually process the input data just like other works, but its depth and kernels are generated by the puppeteer module (realized with Ordinary Differential Equation (ODE)) based on the input complexity each time. By recurrently generating kernel parameters in the puppet module, we can take advantage of the dependence among kernels of different convolutional layers to significantly reduce the size of CNN model by only storing and training the parameters of the much smaller puppeteer ODE module. Through experiments on several datasets, our method has proven to be superior than the traditional CNNs on both performance and efficiency. The model size can be reduced more than 10 times.