Diffusion Model
Diffusion models are a class of generative models in machine learning that simulate a forward diffusion process to gradually add noise to data, and then learn to reverse this process to generate data from noise. This approach involves a sequence of steps (Markov chain) where data is incrementally noised until it becomes indistinguishable from Gaussian noise. The model then learns the reverse process, where it starts with noise and incrementally denoises it to generate samples that resemble the original data distribution.
The training of diffusion models is based on variational inference, optimizing the model to become efficient at reversing the noising process. This methodology allows diffusion models to capture complex, high-dimensional data distributions, making them particularly powerful for tasks like image and audio generation, where they can produce high-quality, diverse samples that closely mimic the characteristics of real-world data.
In computer vision, diffusion models have been used to generate high-fidelity images. For instance, given a dataset of natural landscape photos, a diffusion model can learn the distribution of landscapes and generate new images that look like plausible landscapes despite not being exact replicas of any specific photograph in the training set. This is achieved by first adding noise to the original landscape images in the dataset step by step until only noise remains, and then learning to reverse this process.
During generation, the model starts with a random noise pattern and gradually applies the learned denoising steps to form a new image. Another application is in the field of text-to-image generation, where diffusion models are given textual descriptions and learn to generate images that correspond to those descriptions, showcasing their ability to bridge modalities and understand complex, abstract concepts.