一句话：提出 CNNSplitter，通过模块化分解强 CNN 并与弱 CNN 组合，低代价地修补其在目标类别上的不足。
深度神经网络在实践中并不总是鲁棒，CNN 分类模型常在某些特定类别上表现不佳，而重训练整个模型代价高昂。本文关注修补 CNN 的薄弱部分，受软件工程中模块化与组合思想启发，提出压缩模块化方法 CNNSplitter。该方法将一个强的 N 类 CNN 分解为 N 个较小的 CNN 模块，每个模块包含强模型的一部分卷积核；针对弱模型表现差的目标类别，将弱 CNN 与强模型对应模块组合，从而提升目标类识别能力，并改善非目标类识别，因为被误判为目标类的样本可被正确归类为非目标类。在两个代表性 CNN 和三个常用数据集上的实验显示，目标类精确率和召回率平均提升 12.54% 与 2.14%，非目标类准确率提升 1.18%。
Despite great success in many applications, deep neural networks are not always robust in practice. For instance, a convolutional neuron network (CNN) model for classification tasks often performs unsatisfactorily in classifying some particular classes of objects. In this work, we are concerned with patching the weak part of a CNN model instead of improving it through the costly retraining of the entire model. Inspired by the fundamental concepts of modularization and composition in software engineering, we propose a compressed modularization approach, CNNSplitter, which decomposes a strong CNN model for N-class classification into N smaller CNN modules. Each module is a sub-model containing a part of the convolution kernels of the strong model. To patch a weak CNN model that performs unsatisfactorily on a target class (TC), we compose the weak CNN model with the corresponding module obtained from a strong CNN model. The ability of the weak CNN model to recognize the TC can thus be improved through patching. Moreover, the ability to recognize non-TCs is also improved, as the samples misclassified as TC could be classified as non-TCs correctly. Experimental results with two representative CNNs on three widely-used datasets show that the averaged improvement on the TC in terms of precision and recall are 12.54% and 2.14%, respectively. Moreover, patching improves the accuracy of non-TCs by 1.18%. The results demonstrate that CNNSplitter can patch a weak CNN model through modularization and composition, thus providing a new solution for developing robust CNN models.
@inproceedings{paperbot7214,
title = {Patching Weak Convolutional Neural Network Models through Modularization and Composition},
author = {Binhang Qi and Hailong Sun and Xiang Gao and Hongyu Zhang},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3561153}
}