ISF-GAN: An Implicit Style Function for High-Resolution Image-to-Image Translation by Yahui Liu et al. explained in 5 minutes.

⭐️Paper difficulty: 🌕🌕🌕🌑🌑

ISF-GAN teaser

🎯 At a glance:

I often find myself wishing I knew how to edit images in photoshop but I remember that I already have a full-time job without attempting to learn photoshop. This is where ISF-GAN by Yahui Liu et al. comes in. This new model performs cost-effective multi-modal unsupervised image-to-image translations at high resolution using pre-trained unconditional GANs. ISF-GAN does this by modeling the latent style vector update with an MLP conditioned on a random vector and an attribute code.

⌛️ Prerequisites:

(Highly recommended reading to understand the core contributions of this paper):
1) StyleGAN2
2) StyleFlow (optional)

🚀 Motivation:

Training task-specific GANs in high resolutions is nontrivial and expensive, hence lately the focus has shifted to adapting pretrained unconditional generators with a proven track record (StyleGAN) to various editing and image-to-image tasks. Most methods follow the general pipeline of starting with a latent vector in W+, manipulating it in some way, and passing the updated style code through the pretrained generator to obtain the edited (upscaled, domain-shifted, etc) image. Unfortunately, existing approaches are limited to editing one attribute at a time or change the identity of the person on the photo while applying edits. Moreover, most such methods do not have an obvious way to produce multi-modal results for a single input image. ISF-GAN, on the other hand, uses Adaptive Layer Normalization (AdaLN) in its ISF module for conditional multi-modal latent code editing.

🔍 Main Ideas:

1) Overview:
The goal of this model is to output realistic multi-modal editing results for input images without changing their contents. The proposed way to achieve this goal is with an MLP that transforms the input style vector W+ conditioned on a random vector z and a task-specific vector d.

2) Learning the ISF:
The ISF MLP is trained in an adversarial fashion with a discriminator that classifies the images based on the target attribute d in addition to predicting whether the output image is real or fake. Additionally, the LPIPS, L2, and cycle consistency losses ensure that the content is preserved between images, while a diversity-sensitive loss encourages multi-modal outputs.

3) Injecting the domain and multi-modality:
The authors just switch instance norm for layer norm in the AdaIN and call in AdaLN. The intuition is that channels are correlated in W+ latent codes (the reasoning looks a bit iffy tbh). The complete ISF module is an AdaLN layer between two MLPs and does the following: the domain vector d is concatenated to the random noise vector, goes through the first MLP, and becomes the condition in the AdaLN layer that transforms the input W+ style code before passing it through the second MLP to obtain the final code.

📈 Experiment insights / Key takeaways:
  • ISF-GAN favorably compares to InterFaceGAN, StyleFlow, and StarGAN on a custom synthetic labeled dataset as well as the public test set from StyleFlow in terms of FID, LPIPS, Accuracy, and Arcface (used as a metric, not a loss, huh).
  • The edited attributes are gender, smile, age, and eyeglasses.
  • Mostly standard self-congratulatory GAN results are reported, nothing out of ordinary here.
  • From ablations: content loss helps with interpolation and identity preservation
  • replacing AdaLN with AdaIN decreases FID and FRS

🖼️ Paper Poster:

ISF-GAN paper poster

🛠 Possible Improvements:
  • Authors didn’t really include any
  • I would suggest thinking of a way to incorporate the encoder in an end-to-end fashion (or think of a way to skip it altogether)
✏️My Notes:
  • (3/5) so many utilitarian names lately :(. Although “Implicit Style Function” is clear and concise.
  • Looks like a good (and much simpler IMHO) alternative to StyleFlow
  • I wonder why there aren’t any comparisons to StyleCLIP since the ideas for the latent mapper are so similar
  • I still think the main crux of all these amazing editing approaches is the inability to process real images off-the-shelf without an external encoder, in other words, your editing is only as good as your encoder
  • I am surprised that the arcface loss is not used here at all for identity preservation
  • Do you have any questions left about ISF-GAN? Let’s discuss in the comments!

ISF-GAN arxiv / ISF-GAN github <- Available later?

👋 Thanks for reading!

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