NoiseConditional
NoiseConditional refers to a concept in machine learning, particularly within generative modeling, that describes the process of generating data conditioned on some form of noise. This noise is not necessarily random in the traditional sense but can be a structured input that guides the generation process. In generative adversarial networks (GANs), for example, a latent vector often serves as this noise input, allowing the generator to produce diverse outputs. By manipulating this noise vector, specific attributes or variations of the generated data can be controlled.
This conditioning on noise is crucial for achieving variability and controllability in generated samples. Without it,
The term NoiseConditional is also relevant in diffusion models. In these models, a diffusion process gradually