Generative Model Training With Dual Diffusion-Score Guidance
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Solution Overview
Problem
The optimization process in adversarial training for generative models, particularly generative adversarial networks (GANs), is unstable and prone to crashes, necessitating a more stable and efficient training method.
Innovation Solution
Employ two diffusion models, where one is pre-trained and acts as a teacher model for knowledge distillation, and the other as a student model, to stabilize the training process without adversarial training, using diffusion scores to update the generative model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adversarial training is used for generative models, then the model can learn transformation from noise to data distribution, but the optimization process becomes unstable and training easily crashes
Solution Approach 1:
The patent introduces a diffusion model as an intermediary component between the generative model and the discriminator. This diffusion model stabilizes the training process by providing a controlled diffusion mechanism that prevents the optimization instability and crashes associated with traditional adversarial training, while still enabling the generative model to learn the transformation from noise to data distribution.
Solution Approach 2:
The patent modifies the training approach by changing the parameters and optimization dynamics of the adversarial training process. Specifically, it adjusts how the generative model and discriminator interact by incorporating diffusion-based transformations, which alters the optimization landscape and reduces instability without sacrificing the ability to learn complex data distributions.
2Productivity
If traditional adversarial training is used, then the generative model can be trained, but the training process is inefficient and has high overhead
Solution Approach 1:
The patent applies preliminary action by pre-training the diffusion model on real data before using it in the adversarial training framework. This pre-training step prepares the diffusion model to provide stable and informative gradients during the subsequent generative model training, thereby improving training efficiency and reducing the overall training time required to achieve good performance.
3Reliability
If adversarial training is used, then the generative model can be trained, but the optimization process is unstable
Solution Approach 1:
The diffusion model serves as a mediator that simplifies the training process while improving optimization stability. By introducing this intermediate component, the patent creates a more manageable training dynamics where the diffusion model handles the complex noise-to-data transformation in a controlled manner, making the overall training process more stable and easier to implement without requiring complex stabilization techniques.
Data Source
AI summary
This application provides a generative model training method, and a data conversion method and apparatus. The method includes: using data in a noise set as an input of the generative model, and outputting at least one generated sample, where the generative model is used to perform data conversion on the input data; using the at least one generated sample as an input of a first diffusion model, and outputting at least one first diffusion score, that is, scoring output effect of the generative model based on the first diffusion model; and updating the generative model based on the at least one first diffusion score and at least one second diffusion score output by a second diffusion model, to obtain an updated generative model, where the second diffusion model is obtained through training based on a real sample set.