# Delving StyleGAN Inversion for Image Editing: A Foundation Latent Space Viewpoint

arXiv: https://arxiv.org/abs/2211.11448 · HTML: https://mlsanity.com/paper/2211.11448/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 70 of its 79 references that are in the map.
- First posted on arXiv 21 Nov 2022.

## Builds on (most cited first, 30 of 70)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop](https://mlsanity.com/paper/1506.03365/) (2015)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery](https://mlsanity.com/paper/2103.17249/) (2021)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation](https://mlsanity.com/paper/2008.00951/) (2020)
- [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)
- [DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation](https://mlsanity.com/paper/2110.02711/) (2021)
- [Contrastive Learning of Medical Visual Representations from Paired Images and Text](https://mlsanity.com/paper/2010.00747/) (2020)
- [GANSpace: Discovering Interpretable GAN Controls](https://mlsanity.com/paper/2004.02546/) (2020)
- [Designing an Encoder for StyleGAN Image Manipulation](https://mlsanity.com/paper/2102.02766/) (2021)
- [Pivotal Tuning for Latent-based Editing of Real Images](https://mlsanity.com/paper/2106.05744/) (2021)

## Built on by (most cited first, 18 of 18)

- [Make Encoder Great Again in 3D GAN Inversion through Geometry and Occlusion-Aware Encoding](https://mlsanity.com/paper/2303.12326/) (2023)
- [Transferable Adversarial Facial Images for Privacy Protection](https://mlsanity.com/paper/2408.01428/) (2024)
- [ContextFlow: Training-Free Video Object Editing via Adaptive Context Enrichment](https://mlsanity.com/paper/2509.17818/) (2025)
- [Identity Encoder for Personalized Diffusion](https://mlsanity.com/paper/2304.07429/) (2023)
- [Pedestrian Attribute Editing for Gait Recognition and Anonymization](https://mlsanity.com/paper/2303.05076/) (2023)
- [Learning Common and Salient Generative Factors Between Two Image Datasets](https://mlsanity.com/paper/2512.12800/) (2025)
- [High-Quality Medical Image Generation from Free-hand Sketch](https://mlsanity.com/paper/2402.00353/) (2024)
- [Prediction Exposes Your Face: Black-box Model Inversion via Prediction Alignment](https://mlsanity.com/paper/2407.08127/) (2024)
- [StylePrompter: All Styles Need Is Attention](https://mlsanity.com/paper/2307.16151/) (2023)
- [HyperEditor: Achieving Both Authenticity and Cross-Domain Capability in Image Editing via Hypernetworks](https://mlsanity.com/paper/2312.13537/) (2023)
- [Gradual Residuals Alignment: A Dual-Stream Framework for GAN Inversion and Image Attribute Editing](https://mlsanity.com/paper/2402.14398/) (2024)
- [Training-Free Disentangled Text-Guided Image Editing via Sparse Latent Constraints](https://mlsanity.com/paper/2512.21637/) (2025)
- [SwinStyleformer is a favorable choice for image inversion](https://mlsanity.com/paper/2406.13153/) (2024)
- [Multi-task SAR Image Processing via GAN-based Unsupervised Manipulation](https://mlsanity.com/paper/2408.01553/) (2024)
- [RefineStyle: Dynamic Convolution Refinement for StyleGAN](https://mlsanity.com/paper/2410.06104/) (2024)
- [Unsupervised Panoptic Interpretation of Latent Spaces in GANs Using Space-Filling Vector Quantization](https://mlsanity.com/paper/2410.20573/) (2024)
- [PIDiff: Image Customization for Personalized Identities with Diffusion Models](https://mlsanity.com/paper/2505.05081/) (2025)
- [Motion Transfer-Enhanced StyleGAN for Generating Diverse Macaque Facial Expressions](https://mlsanity.com/paper/2511.16711/) (2025)

## Abstract (arXiv)

GAN inversion and editing via StyleGAN maps an input image into the embedding spaces (W, W^+, and F) to simultaneously maintain image fidelity and meaningful manipulation. From latent space W to extended latent space W^+ to feature space F in StyleGAN, the editability of GAN inversion decreases while its reconstruction quality increases. Recent GAN inversion methods typically explore W^+ and F rather than W to improve reconstruction fidelity while maintaining editability. As W^+ and F are derived from W that is essentially the foundation latent space of StyleGAN, these GAN inversion methods focusing on W^+ and F spaces could be improved by stepping back to W. In this work, we propose to first obtain the precise latent code in foundation latent space W. We introduce contrastive learning to align W and the image space for precise latent code discovery. %The obtaining process is by using contrastive learning to align W and the image space. Then, we leverage a cross-attention encoder to transform the obtained latent code in W into W^+ and F, accordingly. Our experiments show that our exploration of the foundation latent space W improves the representation ability of latent codes in W^+ and features in F, which yields state-of-the-art reconstruction fidelity and editability results on the standard benchmarks. Project page: https://kumapowerliu.github.io/CLCAE.