  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 (CIFAR-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.
