We propose a novel method that trains a conditional Generative Adversarial
Network (GAN) to generate visual interpretations of a Convolutional Neural
Network (CNN). To comprehend a CNN, the GAN is trained with information on how
the CNN processes an image when making predictions. Supplying that information
has two main challenges: how to represent this information in a form that is
feedable to the GANs and how to effectively feed the representation to the GAN.
To address these issues, we developed a suitable representation of CNN
architectures by cumulatively averaging intermediate interpretation maps. We
also propose two alternative approaches to feed the representations to the GAN
and to choose an effective training strategy. Our approach learned the general
aspects of CNNs and was agnostic to datasets and CNN architectures. The study
includes both qualitative and quantitative evaluations and compares the
proposed GANs with state-of-the-art approaches. We found that the initial
layers of CNNs and final layers are equally crucial for interpreting CNNs upon
interpreting the proposed GAN. We believe training a GAN to interpret CNNs
would open doors for improved interpretations by leveraging fast-paced deep
learning advancements. The code used for experimentation is publicly available
at this https URL

本文介绍了一种新方法，使用条件生成对抗网络（GAN）通过信息传递学习卷积神经网络（CNN）的图像处理和预测，同时解决了如何将信息传递到 GAN 的挑战和如何有效地训练 GAN 的问题，该方法通过中间层的平均交互地图来表达 CNN 的结构，通过两种交互方案的对比得出了 GAN 可有效解释 CNN 从而提高图像处理的实现效率。