In this work, we propose a fast and accurate method to reconstruct activations of classification and semantic segmentation networks by stitching them with a GAN generator utilizing a 1x1 convolution. We test our approach on images of animals from the AFHQ wild dataset, ImageNet1K, and real-world digital pathology scans of stained tissue samples. Our results show comparable performance to established gradient descent methods but with a processing time that is two orders of magnitude faster, making this approach promising for practical applications.

翻译：本文提出一种快速且精确的方法，通过将分类网络和语义分割网络与利用1x1卷积的GAN生成器进行拼接，来重建网络激活。我们在AFHQ野生数据集、ImageNet1K的动物图像以及染色组织样本的真实数字病理扫描上测试了该方法。实验结果表明，该方法能达到与经典梯度下降法相当的性能，但处理速度快两个数量级，使其在实际应用中具有广阔前景。