# Learning Inverse Mapping by Autoencoder based Generative Adversarial Nets

arXiv: https://arxiv.org/abs/1703.10094 · HTML: https://mlsanity.com/paper/1703.10094/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2017.

## Built on by (most cited first, 17 of 17)

- [Pivotal Tuning for Latent-based Editing of Real Images](https://mlsanity.com/paper/2106.05744/) (2021)
- [HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing](https://mlsanity.com/paper/2111.15666/) (2021)
- [Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing](https://mlsanity.com/paper/2104.14754/) (2021)
- [Third Time's the Charm? Image and Video Editing with StyleGAN3](https://mlsanity.com/paper/2201.13433/) (2022)
- [TriPlaneNet: An Encoder for EG3D Inversion](https://mlsanity.com/paper/2303.13497/) (2023)
- [Image-Based CLIP-Guided Essence Transfer](https://mlsanity.com/paper/2110.12427/) (2021)
- [State-of-the-Art in the Architecture, Methods and Applications of StyleGAN](https://mlsanity.com/paper/2202.14020/) (2022)
- [Temporally Consistent Semantic Video Editing](https://mlsanity.com/paper/2206.10590/) (2022)
- [Delving StyleGAN Inversion for Image Editing: A Foundation Latent Space Viewpoint](https://mlsanity.com/paper/2211.11448/) (2022)
- [Cycle Encoding of a StyleGAN Encoder for Improved Reconstruction and Editability](https://mlsanity.com/paper/2207.09367/) (2022)
- [In-Domain GAN Inversion for Faithful Reconstruction and Editability](https://mlsanity.com/paper/2309.13956/) (2023)
- [In-N-Out: Faithful 3D GAN Inversion with Volumetric Decomposition for Face Editing](https://mlsanity.com/paper/2302.04871/) (2023)
- [Robust Training Using Natural Transformation](https://mlsanity.com/paper/2105.04070/) (2021)
- [Assessing Neural Network Robustness via Adversarial Pivotal Tuning](https://mlsanity.com/paper/2211.09782/) (2022)
- [Unsupervised Anomaly Appraisal of Cleft Faces Using a StyleGAN2-based Model Adaptation Technique](https://mlsanity.com/paper/2211.06659/) (2022)
- [Generative Adversarial Networks for Dental Patient Identity Protection in Orthodontic Educational Imaging](https://mlsanity.com/paper/2307.02019/) (2023)
- [Gradient Adjusting Networks for Domain Inversion](https://mlsanity.com/paper/2302.11413/) (2023)

## Abstract (arXiv)

The inverse mapping of GANs'(Generative Adversarial Nets) generator has a great potential value.Hence, some works have been developed to construct the inverse function of generator by directly learning or adversarial learning.While the results are encouraging, the problem is highly challenging and the existing ways of training inverse models of GANs have many disadvantages, such as hard to train or poor performance.Due to these reasons, we propose a new approach based on using inverse generator (IG) model as encoder and pre-trained generator (G) as decoder of an AutoEncoder network to train the IG model. In the proposed model, the difference between the input and output, which are both the generated image of pre-trained GAN's generator, of AutoEncoder is directly minimized. The optimizing method can overcome the difficulty in training and inverse model of an non one-to-one function.We also applied the inverse model of GANs' generators to image searching and translation.The experimental results prove that the proposed approach works better than the traditional approaches in image searching.