GAN Weight Balancing for Training Stability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Generative adversarial networks (GANs) face instability during training, leading to mode collapse and low diversity in generated sample images due to vanishing gradient and slow convergence rates, resulting in abnormal adversarial training.
Innovation Solution
The method involves balancing losses between the generator and discriminator by initializing their weights equally and using iterative training with dynamically adjusted learning ratios through gradient descent, ensuring both networks have equal learning abilities and reach a Nash equilibrium.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If free training of GAN is performed, then the generator and discriminator can process adversarial training, but the training process becomes unstable causing mode collapse and low diversity of sample images
Solution Approach 1:
The patent implements dynamic weight adjustment where the discriminator's weight is initialized higher than the generator's weight, and learning rates are adjusted dynamically during training. This dynamic approach prevents the discriminator from becoming too powerful too quickly, maintaining training stability while allowing adversarial training to proceed normally.
Solution Approach 2:
The patent changes key training parameters including weight initialization ratios (discriminator weight > generator weight), learning rate adjustments, and loss function balancing. These parameter changes directly address the instability issue by controlling the relative strength and learning speed of the generator and discriminator throughout training.
2Productivity
If the generator and discriminator train without weight balancing, then training proceeds freely, but vanishing gradient and slow convergence occur
Solution Approach 1:
The patent employs dynamic weight and learning rate adjustment during training. The discriminator starts with higher weight and learning rate, which are gradually reduced as training progresses. This dynamic adjustment ensures stable gradient flow in early stages while enabling faster convergence in later stages, preventing both vanishing gradient and slow convergence issues.
Solution Approach 2:
The patent implements periodic adjustment of training parameters, alternating between phases where the discriminator is strengthened and phases where the generator is strengthened. This periodic action maintains balanced gradient flow throughout training, preventing vanishing gradient while accelerating convergence through structured parameter changes.
3Measurement precision
If the discriminator becomes too powerful during training, then it can easily identify generated images, but the generator cannot learn effectively causing mode collapse
Solution Approach 1:
The patent implements dynamic weight balancing where the discriminator's weight is initialized higher but systematically reduced during training. This allows the discriminator to achieve high identification accuracy in early stages while preventing it from becoming too powerful, maintaining generator learning stability throughout the training process.
Solution Approach 2:
The patent applies partial action by giving the discriminator temporarily excessive weight and learning rate in early training stages, then reducing it. This allows the discriminator to reach high identification accuracy quickly initially, while the subsequent reduction prevents mode collapse and maintains generator learning stability in later stages.
Data Source
AI summary
A method, apparatus, and non-transitory computer readable medium for optimizing generative adversarial network includes determining a first weight of a generator and an equal second weight of a discriminator the first weight is configured to indicate a learning ability of the generator, the second weight is configured to indicate a learning ability of the discriminator; and alternative iteratively training the generator and the discriminator until the generator and the discriminator are convergent.