Spectral Normalization for GAN Discriminator Training Stability
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Solution Overview
Problem
In Generative Adversarial Networks (GANs), the control of the discriminator's performance is challenging, especially in high-dimensional spaces, leading to inaccurate and unstable density ratio estimation, which prevents the generator from learning the multimodal structure of the target distribution and can result in the derivative of the discriminator becoming zero, halting the generator's training.
Innovation Solution
The implementation of spectral normalization on the weight matrices of the discriminator, which constrains the spectral norm to 1, prevents the discriminator from becoming sensitive to a single direction and stabilizes the training process by normalizing the spectral norm using power iteration methods and reparameterization techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the discriminator is trained to accurately discriminate the model distribution from the target distribution in high-dimensional space, then the discrimination performance is improved, but the density ratio estimation becomes unstable and the derivative becomes zero, halting training
Solution Approach 1:
The patent applies spectral normalization by constraining the spectral norm (largest singular value) of the discriminator's weight matrices to be equal to 1. This parameter constraint modifies the discriminator's output range to [-1, 1] and ensures the derivative with respect to input remains non-zero, preventing training collapse while maintaining discrimination capability in high-dimensional spaces
Solution Approach 2:
The patent introduces dynamic adjustment of the discriminator's spectral norm during training. By using power iteration methods to compute and enforce the spectral norm constraint at each training step, the system adapts the discriminator's sensitivity dynamically, preventing it from becoming overly sensitive in any single direction while maintaining overall discrimination performance
2Measurement precision
If the discriminator becomes highly sensitive to a single direction to improve discrimination, then the discrimination capability is enhanced, but the training process becomes unstable and the generator cannot learn the multimodal structure
Solution Approach 1:
By constraining the spectral norm of the weight matrices to 1, the patent limits the maximum amplification factor of the discriminator in any direction. This prevents the discriminator from becoming excessively sensitive to any single input direction, ensuring stable gradients are always available to the generator for learning multimodal structures
Solution Approach 2:
The spectral normalization acts as a counterbalancing mechanism that opposes excessive sensitivity growth in the discriminator. By enforcing the spectral norm constraint, it counterweights the natural tendency of the discriminator to amplify certain directions during training, maintaining balanced and stable learning dynamics
Data Source
AI summary
A model generation method includes updating, by at least one processor, a weight matrix of a first neural network model at least based on a first inference result obtained by inputting, to the first neural network model which discriminates between first data and second data generated by using a second neural network model, the first data, a second inference result obtained by inputting the second data to the first neural network model, and a singular value based on the weight matrix of the first neural network model. The model generation method also includes at least based on the second inference result, updating a parameter of the second neural network model.