# One-shot domain adaptation for semantic face editing of real world images using StyleALAE

arXiv: https://arxiv.org/abs/2108.13876 · HTML: https://mlsanity.com/paper/2108.13876/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 31 references that are in the map.
- First posted on arXiv 31 Aug 2021.

## Builds on (most cited first, 24 of 24)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [Interpreting the Latent Space of GANs for Semantic Face Editing](https://mlsanity.com/paper/1907.10786/) (2019)
- [Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?](https://mlsanity.com/paper/1904.03189/) (2019)
- [In-Domain GAN Inversion for Real Image Editing](https://mlsanity.com/paper/2004.00049/) (2020)
- [StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flows](https://mlsanity.com/paper/2008.02401/) (2020)
- [Few-Shot Adversarial Learning of Realistic Neural Talking Head Models](https://mlsanity.com/paper/1905.08233/) (2019)
- [AttGAN: Facial Attribute Editing by Only Changing What You Want](https://mlsanity.com/paper/1711.10678/) (2017)
- [Few-Shot Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1905.01723/) (2019)
- [VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning](https://mlsanity.com/paper/1705.07761/) (2017)
- [Adversarial Latent Autoencoders](https://mlsanity.com/paper/2004.04467/) (2020)
- [Few-Shot Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.02536/) (2017)
- [Mask-Guided Portrait Editing with Conditional GANs](https://mlsanity.com/paper/1905.10346/) (2019)
- [CONFIG: Controllable Neural Face Image Generation](https://mlsanity.com/paper/2005.02671/) (2020)
- [It Takes (Only) Two: Adversarial Generator-Encoder Networks](https://mlsanity.com/paper/1704.02304/) (2017)
- [One-Shot Domain Adaptation For Face Generation](https://mlsanity.com/paper/2003.12869/) (2020)
- [Interspecies Knowledge Transfer for Facial Keypoint Detection](https://mlsanity.com/paper/1704.04023/) (2017)
- [Learning a Deep Reinforcement Learning Policy Over the Latent Space of a Pre-trained GAN for Semantic Age Manipulation](https://mlsanity.com/paper/2011.00954/) (2020)

## Built on by (most cited first, 1 of 1)

- [CLIP-PAE: Projection-Augmentation Embedding to Extract Relevant Features for a Disentangled, Interpretable, and Controllable Text-Guided Face Manipulation](https://mlsanity.com/paper/2210.03919/) (2022)

## Abstract (arXiv)

Semantic face editing of real world facial images is an important application of generative models. Recently, multiple works have explored possible techniques to generate such modifications using the latent structure of pre-trained GAN models. However, such approaches often require training an encoder network and that is typically a time-consuming and resource intensive process. A possible alternative to such a GAN-based architecture can be styleALAE, a latent-space based autoencoder that can generate photo-realistic images of high quality. Unfortunately, the reconstructed image in styleALAE does not preserve the identity of the input facial image. This limits the application of styleALAE for semantic face editing of images with known identities. In our work, we use a recent advancement in one-shot domain adaptation to address this problem. Our work ensures that the identity of the reconstructed image is the same as the given input image. We further generate semantic modifications over the reconstructed image by using the latent space of the pre-trained styleALAE model. Results show that our approach can generate semantic modifications on any real world facial image while preserving the identity.