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Just FYI, it will less obvious that you’re entrenched and “dug in” to the field of GAN research if you’re less defensive when people say fairly reasonable thing

• godelski 3y ago
  Problem is none of what you believe about me is true. By main focus area is explicit density estimation, GANs are implicit. I like math... But you can check my comment history to see that I really hate hype. I said there are better models for different tasks. Here's a breakdown: - Autoregressive: Best for time sequential data - Normalizing Flows: Best for density estimation and statistics - Diffusion: Best at general image synthesis, editing, and diversity. - GANs: Best when working on edge devices or throughput is critical - VAEs: Best for situations between diffusion and flows, where a implicit PCA is desired You can tell why I discussed GANs because it hit the specific use case. Remember my first comment is literally saying the motivation is trading image quality and diversity for generation time. It's because again, the claim is that there isn't one size fits all models. Such a notion is silly. You don't use a diffusion model to do real time image upscaling (e.g. for video) nor do you want it for RTX supersampling, but you do want diffusion for general image synthesis tasks in such areas like photoshop. Inpainting, outpainting, T2I, unconditional, I2I, and all that you want diffusion because it is much better for those tasks. Just... diffusion requires big models and they're slow and the raspberry pi is small. So you make sacrifices.