A comparative summary of GANs vs diffusion models
| Feature | GANs | Diffusion models |
| Aim | Generate data mimicking training data via a generator-discriminator competition | Generate data by reversing a process that adds noise to data |
| Architecture | Two networks: generator (creates data) and discriminator (evaluates data) | Single network learns to remove noise over many steps |
| Mode collapse | Prone, leading to less diverse outputs | Less prone, ensures diverse sample generation |
| Data efficiency | Requires large datasets to train effectively | More efficient, works well with smaller datasets |
| Input noise | Begins with a noise vector, transformed into data by the generator | Begins with noise, gradually denoised to form data |
| Applications | Image generation, style transfer, super-resolution | Image/audio generation, text-to-image synthesis |
| Other features | Unstable training, single-step generation | Stable training, multistep generation process |
GANs, generative adversarial networks.