Learning Inverse Mapping by AutoEncoder Based Generative Adversarial Nets
Junyu Luo,Yong Xu,Chenwei Tang,Jiancheng Lv +3 more
- 14 Nov 2017
- pp 207-216
TL;DR: 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, which can overcome the difficulty in training and inverse model of an non one-to-one function.
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Abstract: 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.
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Citations
In-Domain GAN Inversion for Real Image Editing
Jiapeng Zhu,Yujun Shen,Deli Zhao,Bolei Zhou +3 more
- 31 Mar 2020
TL;DR: In this article, a domain-guided encoder is proposed to project a given image to the native latent space of GANs and then a domain regularized optimization is performed to fine-tune the code produced by the encoder.
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In-Domain GAN Inversion for Real Image Editing
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Pivotal Tuning for Latent-based Editing of Real Images
TL;DR: Pivotal Tuning Inversion (PTI) as discussed by the authors is a recent approach to bridge the gap between distortion and editability of StyleGAN's latent space, which preserves the editing quality of an in-domain latent region while changing its portrayed identity and appearance.
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Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing
Hyun-Su Kim,Yunjey Choi,Junho Kim,Sungjoo Yoo,Youngjung Uh +4 more
- 01 Jun 2021
TL;DR: In this article, the intermediate latent space has spatial dimensions, and a spatially variant modulation replaces AdaIN, which makes the embedding through an encoder more accurate than existing optimization-based methods while maintaining the properties of GANs.
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