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Has anyone here made the jump from a more customary data science role into something focused on StyleSynthesis, especially generative AI for creative applications? I'm curious about the common skillsets that translate well and what areas require notable upskilling. I've got a solid foundation in Python, ML algorithms, and data manipulation, but I know StyleSynthesis also leans heavily on areas like computer vision, deep learning architectures (GANs, VAEs, diffusion models), and a strong understanding of artistic principles.
Specifically,I'm wondering about the relative importance of having a background in the specific art form you're trying to synthesize (e.g., music theory for audio generation, design principles for image generation). Is it enough to understand the technical aspects of the model, or is some degree of domain expertise crucial for effectively guiding the model and evaluating its output? Also, practical experience seems key. Are there any good starting projects or datasets that would be helpful for building a portfolio in this area? I'm seeing some cool stuff with generating images of faces or animals, and I’m curious how best to start iterating on that. Any insights or experiences would be greatly appreciated!