Deep Propagation Process (DPP)

The Deep Diffusion Process (DDP) model is a generative model that generates data through forward diffusion and reverse diffusion processes. The key concept is to learn the decay of information systems caused by noise and reverse the process to recover information from the noise. This model has powerful generative capabilities.

The DDP model consists of two networks, namely the forward diffusion ladder network and the reverse diffusion ladder network. In the forward diffusion step, input samples are introduced and new samples are obtained by adding noise. While in the back-diffusion step, noise samples are introduced and the original input samples are generated. The model is trained by minimizing the difference between the generated samples and the original samples. This training method can help the model better learn and understand the characteristics of the input data.

Diffusion models not only produce high-quality images, but also have other advantages. Unlike adversarial training, it does not require additional training procedures. In addition, the diffusion model also has unique advantages in terms of training efficiency because of its scalability and parallelization characteristics.

The model used in diffusion model training is similar to the VAE network, but it is simpler and more direct than other network architectures. The size of the input layer is the same as the data dimension, and there can be multiple hidden layers depending on the depth of the network. The middle layer is a linear layer, each layer has its own activation function. The final layer is again the same size as the original input layer to reconstruct the original data. In a denoising diffusion network, the last layer has two independent outputs, one for predicting the mean of the probability density and another for predicting the variance of the probability density. The training process of this model is achieved through maximum likelihood estimation, which optimizes the model parameters by maximizing the likelihood of the observed data. The ultimate goal is to generate samples with a similar distribution to the original data.

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