It is not easy to design and run Convolutional Neural Networks (CNNs) due to: 1) finding the optimal number of filters (i.e., the width) at each layer is tricky, given an architecture; and 2) the computational intensity of CNNs impedes the deployment on computationally limited devices. Oracle Pruning is designed to remove the unimportant filters from a well-trained CNN, which estimates the filters' importance by ablating them in turn and evaluating the model, thus delivers high accuracy but suffers from intolerable time complexity, and requires a given resulting width but cannot automatically find it. To address these problems, we propose Approximated Oracle Filter Pruning (AOFP), which keeps searching for the least important filters in a binary search manner, makes pruning attempts by masking out filters randomly, accumulates the resulting errors, and finetunes the model via a multi-path framework. As AOFP enables simultaneous pruning on multiple layers, we can prune an existing very deep CNN with acceptable time cost, negligible accuracy drop, and no heuristic knowledge, or re-design a model which exerts higher accuracy and faster inference.

本文提出了一种名为 Approximated Oracle Filter Pruning（AOFP）的滤波器裁剪方法，其可以自动化找到卷积神经网络中最不重要的滤波器，同时支持同时对多个层进行修剪，能够在可接受的时间成本内修剪现有的非常深的卷积神经网络，没有启发式知识，或重新设计一个具有更高准确性和更快推断的模型。

用于CNN宽度优化的近似Oracle滤波消减