Photo Editing with Generative Adversarial Networks (Part 2)

In part 1 of this series I introduced Generative Adversarial Networks (GANs) and showed how to generate images of handwritten digits using a GAN. In this post I…

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NVIDIA
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Greg Heinrich
•10 min read•intermediate•
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Computer VisionDeep LearningGenerative Adversarial NetworksTensorFlow

Overview

This article explores the use of Generative Adversarial Networks (GANs) for photo editing, specifically focusing on generating and modifying images of celebrity faces using the CelebA dataset. It discusses the architecture of GANs, the training process, and various applications such as image reconstruction and attribute manipulation.

What You'll Learn

1

How to generate images of celebrity faces using Generative Adversarial Networks

2

How to modify facial attributes in images using latent space manipulation

3

How to visualize the latent space of a GAN to understand feature clustering

Prerequisites & Requirements

  • Understanding of Generative Adversarial Networks and their architecture
  • Familiarity with DIGITS and TensorFlow for training GANs(optional)

Key Questions Answered

What is the CelebA dataset and how is it used in GANs?
The CelebA dataset consists of 200,000 aligned and cropped 178 x 218-pixel RGB images of celebrities, each tagged with up to 40 attributes. It is used to train GANs to generate and manipulate images of celebrity faces.
How does the GAN architecture for image generation work?
The GAN architecture includes a generator (G) and a discriminator (D). The generator creates images from random noise, while the discriminator evaluates the authenticity of images. Both networks are trained simultaneously to improve their performance.
What are the challenges of training GANs on large images?
GANs are notoriously difficult to train on large images due to issues like mode collapse and instability. This article highlights the use of smaller 64x64 pixel images to mitigate these challenges during training.
How can attributes be modified in generated images?
Attributes can be modified by calculating attribute vectors in latent space. For example, to make a face look younger, the average latent vector of non-young images is subtracted from that of young images, allowing for targeted modifications.

Key Statistics & Figures

Number of images in CelebA dataset
200,000
This extensive dataset allows for robust training of GANs, enhancing their ability to generate diverse and realistic images.
Training duration on NVIDIA Titan X
8 hours
This was the time taken to train the GAN for 60 epochs on the 200,000-image dataset.

Technologies & Tools

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AI/ML
Generative Adversarial Networks
Used for generating and modifying images of celebrity faces.
Tools
Digits
Used for training the GAN models on the CelebA dataset.
Tools
Tensorflow
Framework used for building and training the GAN models.

Key Actionable Insights

1
Utilize the CelebA dataset to train your own GAN for image generation tasks.
This dataset provides a rich source of labeled images that can enhance your model's ability to generate realistic faces, making it ideal for projects in computer vision and graphic design.
2
Experiment with manipulating latent vectors to achieve desired facial attributes.
By understanding how to modify attributes in latent space, you can create customized images that meet specific criteria, which is valuable for applications in marketing and entertainment.
3
Leverage tools like TensorBoard for visualizing the latent space of your GAN.
Visualizing the latent space can help you identify how different features cluster together, which is essential for improving model performance and understanding the data.

Common Pitfalls

1
Failing to properly balance the training of the generator and discriminator can lead to mode collapse.
This occurs when the generator produces a limited variety of outputs, which can be mitigated by adjusting the training parameters and ensuring both networks are updated effectively.

Related Concepts

Deep Learning
Computer Vision
Image Processing
Latent Space Representation