  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.
