Conditional Generator Model Training via Dynamic Loss Weighting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for training conditional generative models are not versatile enough to be applied to different GAN variations and often face challenges in converging due to the mixing of loss functions from discriminators and secondary models, especially when output types differ.
Innovation Solution
The proposed method involves iteratively receiving and updating a conditional generator model by determining a discriminator loss and a secondary loss, using a weighted average of these losses to train the model, with the ability to switch weightings between different iterations, and employing pre-trained classification and regression models to refine generator outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If loss functions from discriminators and secondary models are mixed to train the generator, then the generator can be trained to produce realistic and class-specific samples, but the training convergence becomes difficult due to conflicting loss types
Solution Approach 1:
The patent segments the loss function into distinct components: a primary loss function from the discriminator and a secondary loss function from classification or regression models. These are combined through a controllable mixing parameter, allowing independent optimization of each loss type while maintaining overall training stability and convergence.
2Adaptability or versatility
If the generator is trained with multiple loss functions simultaneously, then class-specific sample generation is improved, but the versatility of the training method across different GAN variations is limited
Solution Approach 1:
The patent creates a universal training framework where the same core methodology can be applied to various GAN variations (e.g., conditional GANs, progressive GANs) by adjusting the primary and secondary loss functions. The controllable mixing parameter allows adaptation to different GAN types while maintaining consistent training convergence behavior.
3Adaptability or versatility
If fixed weightings are used for combining discriminator loss and secondary loss, then the training process is simplified, but the ability to optimize for different training stages and GAN variations is reduced
Solution Approach 1:
The patent introduces a controllable mixing parameter that can be dynamically adjusted during training to optimize the balance between primary and secondary loss functions. This dynamic weighting allows adaptation to different training stages and GAN variations without requiring complex multi-parameter optimization, maintaining simplicity while providing flexibility.
Data Source
AI summary
Systems and methods for training a conditional generator model are described. Methods receive a sample, and determine a discriminator loss for the received sample. The discriminator loss is based on an ability to determine whether the sample is generated by the conditional generator model or is a ground truth sample. The method determines a secondary loss for the generated sample and updates the conditional generator model based on an aggregate of the discriminator loss and the secondary loss.