论文标题
新型的自适应二进制搜索策略 - 基于群集和聚类的CNN滤波器修剪法没有参数设置
Novel Adaptive Binary Search Strategy-First Hybrid Pyramid- and Clustering-Based CNN Filter Pruning Method without Parameters Setting
论文作者
论文摘要
CNN模型中修剪冗余过滤器的修剪已受到越来越多的关注。在本文中，我们提出了一种自适应二进制搜索 - 基于基于聚类的金字塔和基于聚类的金字塔（基于ABSHPC）的方法，以自动修剪过滤器。在我们的方法中，对于每个卷积层，最初构建了混合金字塔数据结构来存储每个滤镜的层次信息。给定耐受精度损失，没有参数设置，我们从最后一个卷积层开始到第一层。对于相对于上一层的每个被考虑的层，我们基于ABSHPC的过程都将所有过滤器应用于群集，因此，每个群集都由滤波器中间均值表示混合金字塔的中间均值表示，从而导致最大程度地去除冗余过滤器。基于实用的数据集和CNN模型，具有较高精度，详尽的实验结果证明了相对于先进方法，提出的滤波器修剪方法的显着参数和浮点操作降低了优点。
Pruning redundant filters in CNN models has received growing attention. In this paper, we propose an adaptive binary search-first hybrid pyramid- and clustering-based (ABSHPC-based) method for pruning filters automatically. In our method, for each convolutional layer, initially a hybrid pyramid data structure is constructed to store the hierarchical information of each filter. Given a tolerant accuracy loss, without parameters setting, we begin from the last convolutional layer to the first layer; for each considered layer with less or equal pruning rate relative to its previous layer, our ABSHPC-based process is applied to optimally partition all filters to clusters, where each cluster is thus represented by the filter with the median root mean of the hybrid pyramid, leading to maximal removal of redundant filters. Based on the practical dataset and the CNN models, with higher accuracy, the thorough experimental results demonstrated the significant parameters and floating-point operations reduction merits of the proposed filter pruning method relative to the state-of-the-art methods.
