Decompressiemodels

Decompressiemodels, often referred to as diffusion models, are a class of generative models in machine learning that learn to create data, such as images, by reversing a gradual corruption process. The core idea is to start with random noise and iteratively denoise it until a coherent piece of data is formed. This denoising process is guided by a neural network that has been trained to predict the noise that was added at each step.

The training of a decompressiemodel involves two main phases. First, a forward diffusion process is applied to the training data, where noise is progressively added over a series of timesteps until the original data is completely obscured by pure noise. Second, a reverse diffusion process is learned. A neural network, typically a U-Net architecture, is trained to predict the noise that was added at each step of the forward process. By learning to accurately predict and subtract this noise, the model can effectively reverse the diffusion process.

During inference, the model starts with a sample of pure noise and iteratively applies the learned denoising steps. At each timestep, the model predicts the noise component and subtracts it from the current noisy sample, gradually transforming the noise into a realistic data sample that resembles the training data distribution. This iterative refinement allows decompressiemodels to generate high-quality and diverse outputs. Their ability to produce photorealistic images has made them a prominent area of research in generative artificial intelligence.