## 1 Introduction
In this paper, we also use different pruning rates for each convolution layer, which is in fact automatically identified by the proposed iterative pruning algorithm. The main focus of any pruning algorithm is to minimize network loss while removing as many filters as possible. Consequently, in recent years loss-aware filter pruning methods are gaining popularity. The proposed pruning algorithm, in every pruning iteration, globally searches for the set of filters from the whole network which is least important in terms of overall network loss. The pruning algorithms presented by [PERSON] et al. [13, 15, 16] claim that soft filter pruning, i.e., keeping track of pruned filters and updating them during training, has proven to be more effective as compared to the conventional train-prune-retrain pipeline. But, the disadvantage is that filter selection and pruning from the original network should be performed in every training epoch resulting in increased computational cost. Therefore in this paper, we used a hard filter pruning approach, i.e., whenever the filter is selected for pruning, the selected filter along with its connections in the network are permanently removed. The advantage of this approach is that once the pruning is completed the resulting slimmed network with reduced complexity can be used for final retraining. Moreover, the proposed technique is evaluated on relatively simple VGGNet to complex ResNet architectures using CIFAR-10 and ImageNet benchmark datasets. The main contributions of this paper are summarized as follows:  
* We analyze several magnitudes and similarity based filter selection criteria and utilized both criteria in designing the pruning algorithm. The filters are pruned in a hard manner, i.e., if the filter is selected for pruning, it is permanently removed and the network is rearranged for further processing. * Loss-aware variable rate pruning is used across different layers of the network which is more effective compared to fixed-rate pruning. The optimal pruning rate for each layer is automatically identified. * To show the effectiveness of the proposed pruning algorithm the commonly used VGGNet and ResNet are evaluated on CIFAR-10 and ImageNet benchmark datasets. Detailed analysis of the pruning and retraining process shows how to reduce the overall cost of the pruning algorithm. The remainder of this paper is organized as follows: Section 2 describes some relevant prior work in network pruning. Then, we present a detailed process of the proposed pruning algorithm, MSVFP, in Section 3. This section includes several filter selection criteria along with a detailed pruning algorithm. In Section 4, a discussion on the experimental evaluation of our method in comparison with state-of-the-art (SOTA) methods is presented. We finally conclude the proposed algorithm in Section 5. Additionally, the implementation code for this paper has been open sourced and available online ([[https://github.com/ghimiredhikura/MSVFP-FilterPruning](https://github.com/ghimiredhikura/MSVFP-FilterPruning)]([https://github.com/ghimiredhikura/MSVFP-FilterPruning](https://github.com/ghimiredhikura/MSVFP-FilterPruning)), accessed on 6 December 2022).