difusioonimudelitel

Diffusion models are a class of generative models in machine learning that have gained significant popularity for their ability to produce high-quality synthetic data, particularly images. The core idea behind diffusion models is to gradually add noise to data until it becomes pure noise, and then learn to reverse this process, starting from noise and progressively denoising it to generate a sample.

This process is typically divided into two main phases. The forward diffusion process systematically adds Gaussian noise to the data over a series of steps, gradually destroying its structure. The reverse diffusion process, which is the learned component, aims to undo this noise addition. It is trained to predict the noise that was added at each step and subtract it, thereby reconstructing the original data distribution.

The mathematical formulation often involves a Markov chain where each step is conditioned on the previous one. The model learns the conditional probability distribution of the data at a slightly less noisy state, given the current noisy state. This is usually achieved using a neural network, often a U-Net architecture, which takes the noisy data and the current timestep as input and outputs a prediction of the noise or the denoised data.

Once trained, a diffusion model can generate new data by starting with random noise and iteratively applying the learned reverse diffusion process. This iterative refinement allows for the generation of highly detailed and realistic samples. Their effectiveness has led to applications in image generation, audio synthesis, video generation, and even drug discovery.