Feedback Control for Online Training of Neural Networks

Zilong Zhao 1, Sophie Cerf 1, Bogdan Robu 1 and Nicolas Marchand 1 Abstract -- Convolutional neural networks (CNNs) are commonly used for image classification tasks, raising the challenge of their application on data flows. During their training, adaptation is often performed by tuning the learning rate. Usual learning rate strategies are time-based i.e. monotonously decreasing. In this paper, we advocate switching to a performance-based adaptation, in order to improve the learning efficiency. We present E (Exponential)/PD (Proportional Derivative)-Control, a conditional learning rate strategy that combines a feedback PD controller based on the CNN loss function, with an exponential control signal to smartly boost the learning and adapt the PD parameters. Stability proof is provided as well as an experimental evaluation using two state of the art image datasets (CIF AR-10 and Fashion-MNIST). Results show better performances than the related works (faster network accuracy growth reaching higher levels) and robustness of the E/PD-Control regarding its parametrization. I NTRODUCTION Convolutional neural networks (CNNs) are popular machine learning algorithms for image classification, as they are well suited for visual pattern recognition and require low preprocessing [1].