As a Data Scientist at DeepMind Technologies, I had the chance to work on a fascinating project to generate artistic images through Generative Adversarial Networks (GANs) for our client in the advertising industry. We employed a StyleGAN, which allows for high-resolution images with more control over the generated output.
I was responsible for training the generator and discriminator using tons of artistic images provided by the client, and it was a delicate balancing act to ensure that neither becomes 'too powerful'. My role included data preprocessing, model architecture design, and fine-tuning the model's parameters for optimal results.
The project was appreciated by the client as it reduced their costs on stock photos and gave them unique images to use in their ads. This enhanced the brand's creative appeal and saved them approximately 30% on image-related costs, demonstrating the power of GANs in creative and cost-efficient solutions.