MOFP: Multi-Objective Filter Pruning for Deep Learning Models

The paper proposes a new approach called Multi-Objective Filter Pruning (MOFP), which formulates the filter pruning of deep learning models as a multi-objective optimization problem. The proposed approach applies the Non-Dominated Sorting Genetic Algorithm II to solve the problem and the Asymmetric Gaussian Distribution (AGD) for population initialization. Compared with existing methods, MOFP shows competitiveness in terms of a balance of objectives between compression rates, computing power, and prediction accuracy. In addition, the search result of MOFP is a Pareto Front, which eliminates the need for multiple searches to obtain architectures with different compression rates, significantly improving overall search efficiency. The results show that the use of AGD for population initialization can enhance the search process by effectively exploring the search space, leading to higher quality results.