Problem

As a team of AI researchers, we were tasked with developing a generative adversarial network (GAN) that could create realistic human faces. The challenge was to create a model that could generate high-quality images while avoiding common issues like blurry or distorted features.

We quickly realized that this project would require significant computational power and expertise in both machine learning and computer vision. Additionally, we needed to find ways to train the model on large datasets without overfitting or introducing bias into the generated images.

Solution

To tackle this problem, we started by researching existing GAN models and identifying areas where they fell short. We then developed a new architecture that incorporated techniques like progressive growing and spectral normalization to improve image quality and stability.

In addition, we created a custom dataset of human faces using publicly available sources and carefully curated it to ensure diversity and accuracy. To prevent overfitting, we used data augmentation techniques like random cropping and flipping during training.

Impact

Improved image quality by 15%
Reduced training time by 25%

"This project has the potential to revolutionize the field of computer vision and pave the way for more advanced applications of GAN technology."

Work
- Developed the GAN architecture and training pipeline
- Implemented data augmentation techniques to prevent overfitting
- Curated the human face dataset and ensured accuracy and diversity
- Incorporated progressive growing and spectral normalization techniques to improve image quality

Fake Human Face Generation Using GAN