Diffusion models, which are increasingly used in generating images and other data, typically rely on a few key loss functions during training. The most common loss function used is the Mean Squared Error (MSE) loss. In the context of diffusion models, this loss is employed to measure the difference between the predicted noise and the actual noise added to the input data. By doing this, the model learns to effectively reverse the diffusion process and produce more accurate predictions of the original data.
Another important aspect of training diffusion models is the incorporation of denoising objectives. Denoising Score Matching is often used in conjunction with MSE. This technique involves training the model to estimate the gradient of the data distribution based on noisy input samples. The essence of this approach is to enable the model to recover clean signals from noisy observations by maximizing the likelihood of the data distribution. In practice, the denoising score matching focuses on minimizing the distance between the model's noise predictions and the actual noise levels at various time steps, further refining its ability to generate accurate samples.
Additionally, some variations of loss functions, such as perceptual losses, can be considered, especially in applications related to image generation. These perceptual loss functions are based on high-level feature representations rather than pixel-wise differences, helping to ensure that the generated images are not just quantitatively close to the targets but also look visually appealing. Overall, while MSE and denoising score matching are the foundation of loss functions in training diffusion models, exploring perceptual losses can enhance the output quality, leading to superior results in practical applications.