AI Model Training with Domain-Similarity Gradients for Faster Adaptation
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Solution Overview
Problem
Diffusion probabilistic models for image generation require significant computational resources and lengthy training times due to their step-by-step image generation process.
Innovation Solution
A method for training a pre-trained AI model by generating images in different domains, determining gradients of loss functions for these images, calculating similarities between these gradients, and updating the model's parameters based on these similarities to adapt it to a target domain, thereby optimizing the training process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If diffusion probabilistic models are used for image generation, then image generation quality is improved, but computational amount and training time increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training the AI model on a source domain before adapting it to the target domain. The pre-training phase prepares the model with general image generation capabilities, which are then fine-tuned for specific target domain requirements, reducing the overall training time while maintaining image quality
Solution Approach 2:
The training process is segmented into distinct phases: source domain pre-training, domain similarity calculation, and target domain adaptation. This segmentation allows each phase to be optimized independently, with the model learning general features first and then adapting to specific domain characteristics, thereby reducing total computational time
2Ease of operation
If diffusion probabilistic models generate images step by step from noise, then image generation controllability is improved, but computational amount increases
Solution Approach 1:
The model performs preliminary learning of the diffusion process dynamics during source domain pre-training, establishing the step-by-step generation framework once. This preliminary setup enables efficient image generation in the target domain without repeating the computationally intensive process initialization, reducing overall computational energy while preserving controllability
3Productivity
If transfer learning is performed without domain similarity consideration, then model adaptation speed is improved, but adaptation accuracy to target domain decreases
Solution Approach 1:
The patent implements feedback by calculating domain similarity between source and target domains and using this information to adjust the adaptation process. The similarity metric provides feedback on how much the model should be adjusted, enabling accurate adaptation while maintaining efficient transfer learning speed
Solution Approach 2:
The adaptation process dynamically changes model parameters based on calculated domain similarity. When similarity is high, fewer parameters are adjusted; when similarity is low, more parameters are fine-tuned. This parameter adaptation strategy maintains both speed and accuracy in the transfer learning process
Data Source
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AI summary
A method and apparatus for training a pre-trained Al model are provided. The method for training the pre-trained AI model includes: generating, by the pre-trained Al model, a first image and a second image; determining a first gradient of a first loss function of the first image and a second gradient of a second loss function of the second image; determining a similarity between the first gradient and the second gradient; and updating the pre-trained Al model based on the similarity, wherein the first image and the second image respectively correspond to different domains.