KRR-CNN: kernels redundancy reduction in convolutional neural networks

Convolutional neural networks (CNNs) are a promising tool for solving real-world problems. However, successful CNNs often require a large number of parameters, which leads to a significant amount of memory and a higher computational cost. This may produce some undesirable phenomena, notably the overfitting. Indeed, in CNNs, many kernels are usually redundant and can be eliminated from the network while preserving the performance. In this work, we propose a new optimization model for kernels redundancy reduction in CNN named KRR-CNN. It consists of minimization and optimization phases. In the first one, a dataset is used to train a specific CNN generating a learned CNN with optimal parameters. These later are combined with a decision optimization model to reduce kernels that have not contributed to the first task. The optimization phase is carried out by the evolutionary genetic algorithm. Efficiency of KRR-CNN has been demonstrated by several experiments. In fact, the suggested model allows reducing the kernels redundancy and improving the classification performance comparable to the state-of-the-art CNNs.