Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time
Computer Vision and Pattern Recognition
2024-03-19 v2 Artificial Intelligence
Machine Learning
Image and Video Processing
Abstract
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.
Keywords
Cite
@article{arxiv.2302.02181,
title = {Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time},
author = {Rudolf Herdt and Maximilian Schmidt and Daniel Otero Baguer and Jean Le'Clerc Arrastia and Peter Maass},
journal= {arXiv preprint arXiv:2302.02181},
year = {2024}
}