Course Content
Image Synthesis Through Generative Networks
Image Synthesis Through Generative Networks
Diffusion Models
Diffusion models are a class of generative models used to create data by modeling the process of gradually transforming simple noise into complex, structured data.
Key Concepts
The key concepts of diffusion models include:
Forward diffusion process
In the forward process, data (like an image) is gradually corrupted by adding noise over several steps until it becomes almost indistinguishable from pure noise. This process usually has a specific design, where each step only depends on the immediate previous step.
Reverse diffusion process
The reverse process aims to undo the forward diffusion, starting from pure noise and gradually removing the noise to reconstruct the original data. The goal is to model the reverse transitions accurately so that starting from a noise sample can eventually produce a sample from the original data distribution.
Training objective
The model learns the reverse process by training on the corrupted data. It tries to predict the original data from the noisy data at each step. Typically, the model minimizes a loss function that measures the difference between the predicted data and the actual original data at each step.
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