EditGAN: High-Precision Semantic Image Editing

Introduction

require large-scale datasets with semantic segmentation annotations for training
only provide high-level control
simply interpolate between different images

wandb.init(project='editGAN', config={'lr': 0.01, 'nepochs': 10})
for _ in range(10):
    wandb.log({'loss': loss})

Existing Approaches––and Their Drawbacks

Some methods such as MaskGAN, Cascade EF-GAN, SEAN, and DeepFaceDrawing rely on GANs conditioning on class labels or pixel-wise semantic segmentation annotations where different conditionings lead to modifications in the output, while others use auxiliary attribute classifiers to guide synthesis and edit images. However such methods have a few drawbacks:
Training these conditional GANs or external classifiers requires large annotated datasets. Therefore, these methods are currently limited to image types for which large annotated datasets are available (like portraits).
Furthermore, even if annotations are available, most techniques offer only limited editing control, since these annotations usually consist only of high-level global attributes or relatively coarse pixel-wise segmentation.
Methods such as Editing in Style, SEAN, and VOGUE focus on mixing and interpolating features from different images, thereby requiring reference images as editing targets and usually also not offering fine control.
Methods such as InterfaceGAN, GAN Dissection, and GANSpace that focus on carefully analyzing and dissecting the latent spaces of the GAN aimed at finding disentangled latent variables suitable for editing or methods focused on controlling the network parameters of the GANs are not user-friendly and cannot provide users with the desired level of flexibility and details.

The Novelty of EditGAN

Significant Advantages of EditGAN

EditGAN offers a very high-precision editing experience without having to rely on carefully analyzing and dissecting the latent spaces like InterfaceGAN, GAN Dissection, and GANSpace.
It requires very little annotated training data and does not rely on external classifiers unlike MaskGAN, Cascade EF-GAN, SEAN, and DeepFaceDrawing.
EditGAN can be run interactively in real-time.
It allows for straightforward compositionality of multiple edits by learning editing vectors in latent space.
It works on real embedded, GAN-generated, and even out-of-domain images.

Okay. So How Does EditGAN Work?

Experiments

Car Images with a spatial resolution (384, 512)
Bird Images with a spatial resolution (512, 512)
Cat Images with a spatial resolution (256, 256)
Face Images with a spatial resolution (1024, 1024)

Qualitative Results

Examples of Segmentation-driven Edits with EditGAN

Examples of Combining Multiple Edits

Examples of High-Precision Editing

Large-Scale Modifications

Out-of-Domain Results

Challenging Editing Operations

Beak Size Editing

License Plate Removal

Quantitative Results

Semantic Correctness: Using a pre-trained smile attribute classifier, it is measured whether the faces show smiling expressions after editing.
Distribution-level Image Quality: Fréchet Inception Distance and Kernel Inception Distance are calculated between 400 edited test images and the CelebA-HD test dataset.
Identity Preservation: Using the pre-trained ArcFace feature extraction network, it is measured whether the identity of the subjects is maintained when applying the edit which specifically is the cosine-similarity between original and edited images.

Key Impacts of EditGAN

Advantages

Where previous generative modeling-based image editing methods offer only limited high-level editing capabilities, EditGAN provides users unprecedented high-precision semantic editing possibilities.
The techniques proposed in the paper can be used for artistic purposes and creative expression and benefits designers, photographers, and content creators. Such type of AI-driven image editing tools can potentially democratize high-quality image editing.
On a larger scale, the ability to synthesize data with specific attributes can be leveraged in training and fine-tuning machine learning models.

Disadvantages

The high-precision photo editing offered by EditGAN can potentially be used for advanced photo manipulation for nefarious purposes. The recent progress of generative models and AI-driven photo editing has profound implications on image authenticity and beyond. As one potential way to tackle these challenges, the research community has already been making significant efforts for developing methods for automatically validating real images and detecting manipulated or fake images such as
Deep Learning for Deepfakes Creation and Detection: A Survey
The Creation and Detection of Deepfakes: A Survey
Generative models like EditGAN are usually only as good as the data they were trained on. Therefore, biases in the underlying datasets are still present in the synthesized images and preserved even when applying our proposed editing methods. It is therefore important to be aware of such biases in the underlying data and counteract them. By actively collecting more representative data or by using bias correction methods this problem can be tackled to some extent as shown by the following papers:
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting
Fair Generative Modeling via Weak Supervision
Inclusive GAN: Improving Data and Minority Coverage in Generative Models
Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks

Conclusion

We discuss the paper EditGAN: High-Precision Semantic Image Editing, which proposes EditGAN, a novel method for high-precision, high-quality semantic image editing.
EditGAN relies on a GAN that jointly models RGB images and their pixel-wise semantic segmentation maps and that requires only very few annotated data for training.
Editing is achieved by performing optimization in latent space while conditioning on edited segmentation masks.
This optimization can be amortized into editing vectors in latent space, which can be applied to other images directly, allowing for real-time interactive editing without any or only a little further optimization.
The authors demonstrate a broad variety of editing operations on different kinds of images, achieving an unprecedented level of flexibility and freedom in terms of editing while preserving high image quality.

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