Layer-Specific Gradient Multipliers for GAN Training Stability
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Solution Overview
Problem
Generative adversarial networks (GANs) face instability and mode collapse during training, where the generator network becomes fixated on a limited subset of training data modes, resulting in repetitive output, making it difficult to generate diverse outputs across all modes.
Innovation Solution
Assigning multipliers to the layers of the generator and discriminator networks during back-propagation, with larger multipliers closer to the output layer, helps slow down convergence and prevent mode collapse by adjusting node weights effectively, ensuring the generator produces a range of outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed learning rate is applied uniformly to all layers during back-propagation, then the training process is simple to implement, but mode collapse occurs and the generator becomes fixated on limited training data modes
Solution Approach 1:
The patent applies different learning rates to different layers of the neural network based on their depth. Specifically, layers closer to the input receive smaller learning rates while layers closer to the output receive larger learning rates. This local differentiation of training parameters prevents mode collapse by ensuring that earlier layers learn stable features while later layers can explore diverse output modes, thereby improving both training stability and output diversity.
Solution Approach 2:
The patent dynamically adjusts the learning rate parameter based on layer depth during the training process. Instead of using a uniform fixed learning rate, the system modifies the learning rate parameter to vary across layers, with the learning rate increasing as a function of layer depth. This parameter change enables the network to achieve stable convergence while maintaining the ability to generate diverse outputs across all training modes.
2Productivity
If the generator network converges quickly during training, then training efficiency is improved, but the generator becomes fixated on a single mode and loses ability to generate diverse outputs
Solution Approach 1:
The patent implements local quality by applying different learning rates to different layers. Deeper layers (closer to output) have larger learning rates that allow them to continue exploring diverse modes even as training progresses, while shallower layers have smaller learning rates that provide stability. This enables the network to maintain fast convergence overall while preserving the ability to generate diverse outputs across all modes.
Solution Approach 2:
The learning rate is made dynamic rather than static, varying by layer depth. This dynamic adjustment allows the training process to adapt at different stages: early layers converge quickly for stability while later layers maintain higher plasticity to explore diverse modes. The dynamic learning rate structure enables the system to achieve both fast training and comprehensive mode coverage.
Data Source
AI summary
Improving the training of generative adversarial networks by identifying the input and output layers in the generator network, assigning a gradient multiplier to each layer, and multiplying the gradient by the multiplier during back-propagation and before applying the product of the gradient and multiplier to node weights of the layers.