At Akvelon, we pride ourselves on delivering cutting-edge technology solutions that push the boundaries of innovation. In April 2020, we introduced AI ArtDive, an application that revolutionizes photo editing by leveraging the power of artificial intelligence (AI). One of the standout features of AI ArtDive is the Gender Swap GAN filter, which enables users to transform the faces of subjects in photos to look like the opposite gender. In this article, we will delve into the methodology and approach behind the development of this remarkable filter, highlighting the techniques and neural networks employed to achieve outstanding results.
The Power of Generative Adversarial Networks (GANs)
Generative adversarial networks (GANs) have emerged as a powerful tool in the field of AI, allowing us to generate realistic images by pitting two neural networks against each other. The GAN architecture consists of a generator and a discriminator. The generator's role is to create images that meet specific parameters, while the discriminator's task is to determine whether an image is generated or real. Through a process of competition, the generator learns to create images that successfully deceive the discriminator, resulting in increasingly realistic outputs.
Building the Dataset
To train the Gender Swap GAN filter effectively, we needed a diverse dataset consisting of numerous photos of people's faces. While there are existing photo datasets available, they often lack suitability or have poor image quality for our specific task. To ensure the quality of our target images, we employed the RetinaFace neural net, which allowed us to extract high-quality facial images from our dataset. Additionally, we utilized the InsightFace gender detection model to verify the gender of the target subjects, ensuring accurate gender distinction during the style transfer process.
Two Approaches: Supervised and Unsupervised Training
During our research, we explored two distinct approaches to training the Gender Swap GAN filter: supervised and unsupervised.
Supervised Approach: In the supervised approach, we trained the neural network using pairs of images, each depicting the same person as both male and female. This approach required a meticulously prepared dataset, where the correlation between different appearances of the same person could be learned effectively. For this approach, we adopted the pix2pixHD neural net architecture, a conditional GAN framework designed for image-to-image translation. The generator in pix2pixHD translates semantic label maps into realistic images, while the discriminator distinguishes between real and translated images.
Unsupervised Approach: In contrast, the unsupervised approach aimed to transfer style between unpaired images. By randomly selecting images of men and women from our dataset, we trained the network to learn how to transform the style of one image to match the other. For this approach, we utilized a modification of CycleGAN known as UNIT (Unsupervised Image-to-Image Translation Networks). CycleGAN consists of two generators and two discriminators, with each generator responsible for transferring style between labeled images. The discriminator ensures the fidelity of the generated images.
Results and Conclusion
After extensive research and development, we found that the supervised approach, although requiring more carefully curated data, yielded superior results for gender-swapping applications. The use of paired images allowed for a more precise correlation between different appearances of the same person, resulting in highly accurate gender transformations.
The introduction of AI ArtDive with the Gender Swap GAN filter has revolutionized the world of photo editing. Users can now effortlessly transform the faces of subjects to look like the opposite gender, opening up creative possibilities and showcasing the power of AI-driven image manipulation.
In conclusion, the Gender Swap GAN filter developed by Akvelon through extensive research and advanced neural networks has propelled AI ArtDive to the forefront of photo editing technology. The combination of supervised and unsupervised training approaches, along with the utilization of state-of-the-art GAN architectures, has enabled us to deliver remarkable results in gender-swapping applications. We are proud to contribute to the advancement of AI technology and look forward to further innovations in the field.
- Ming-Yu Liu, Thomas Breuel, Jan Kautz, "Unsupervised Image-to-Image Translation Networks," Arxiv, 2018.
- Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros, "Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks," ICCV 2017.
- Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros, "Image-to-Image Translation with Conditional Adversarial Nets," CVPR 2017.
- Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, Bryan Catanzaro, "High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs," CVPR 2018.
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