GAN Loss Optimization via Genetic Algorithm
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Solution Overview
Problem
Generative Adversarial Networks (GANs) face instability and training difficulties, including mode collapse, low variability datasets, artifacts, and blurring, due to their inherent instability and the need for manual, laborious processes in creating new formulations, which limits their widespread use.
Innovation Solution
The Genetic Loss Optimization (GLO) framework uses a genetic algorithm to jointly optimize the loss functions of GANs' discriminator and generator networks, allowing for the automated discovery and optimization of candidate loss functions, activation functions, and learning rate schedules, thereby avoiding biases and assumptions inherent in hand-designed formulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes are used to create new GAN formulations, then theoretical reasoning and assumptions can be incorporated, but the process becomes laborious and inflexible
Solution Approach 1:
The system performs automated self-optimization of GAN formulations through genetic algorithms that automatically evolve loss functions, activation functions, and hyperparameters without requiring manual theoretical reasoning or intervention
Solution Approach 2:
The manual mechanical process of theoretically designing GAN formulations is replaced with an automated computational system using genetic algorithms and machine learning to discover optimal formulations
2Reliability
If existing GAN variants are used to address training issues, then specific problems like mode collapse can be targeted, but instability and training difficulties persist
Solution Approach 1:
The system dynamically adapts GAN formulations by automatically evolving loss functions, activation functions, and hyperparameters based on performance feedback, allowing the model to optimize for different metrics and conditions rather than using fixed variants
Solution Approach 2:
The system systematically explores and optimizes multiple parameters including loss function coefficients, activation function choices, learning rate schedules, and network architecture parameters to achieve both stability and adaptability
3Productivity
If automated optimization is applied to GAN formulations, then laborious manual processes are eliminated, but the complexity of optimizing multiple functions increases
Solution Approach 1:
The complex optimization problem is segmented into three separate but coordinated optimization processes: loss function optimization, activation function optimization, and hyperparameter optimization, each handled by specialized genetic algorithms that work in parallel
Solution Approach 2:
A universal genetic algorithm framework is applied that can optimize multiple different aspects of GAN formulations (loss functions, activation functions, hyperparameters) using the same underlying optimization methodology, simplifying the overall system design
Data Source
AI summary
A process for discovering optimal Generative Adversarial Networks (GAN) includes jointly optimizing the three functions of a GANs process including (i) a real component of a discriminator network's loss that is a function of D(x), wherein D(x) is the discriminator network's output for a real sample from an input dataset; (ii) a synthetic component of the discriminator network's loss that is a function of D(G(z)), wherein D(G(z)) is the discriminator network's output for a generator network's synthetic samples z from a latent distribution; and (iii) a generator network's loss which is a function of D(G(z)), with the discriminator network's total loss being the sum of components (i) and (ii). And separately optimizing each of the three functions in accordance with a process for evolving candidate loss functions having tree format by a genetic algorithm and optimizing selected candidate loss functions by optimizing set of coefficients of each of the one or more best candidate loss functions, wherein the set of coefficients is represented by a vector with dimensionality equal to the number of nodes in the best candidate loss function's tree.