Generative Adversarial Networks (GANs) form a class of powerful generative models that learn to produce realistic synthetic data by setting up a competitive process between two neural networks.
This adversarial framework enables GANs to model complex data distributions and generate highly convincing outputs such as images, audio, and text.
However, training GANs is inherently challenging due to instability, mode collapse, and convergence difficulties. Stable training strategies have been developed to mitigate these challenges and make GANs more reliable and effective in practice.
Generative Adversarial Networks
GANs consist of two neural networks trained simultaneously:
The generator improves by learning to fool the discriminator, while the discriminator enhances its ability to identify fakes. This dynamic encourages the generator to produce increasingly realistic synthetic data.
GAN Architecture and Objective
The GAN training objective is a two-player minimax game defined as:
Where.
Challenges in GAN Training
Listed here are major obstacles that affect the stability and effectiveness of GAN training. They reflect the sensitivity of GANs to model design, tuning, and training dynamics.
1. Instability: Oscillations during training can prevent convergence.
2. Mode Collapse: Generator produces a limited variety of outputs, losing diversity.
3. Vanishing Gradients: A poor discriminator makes generator updates ineffective.
4. Sensitive Hyperparameters: Learning rates and architecture choices critically affect performance.
Stable Training Strategies
To address these difficulties, many strategies have been proposed:
1. Loss Function Variants:
Wasserstein GAN (WGAN): Uses the Earth-Mover distance for more stable gradients.
Least Squares GAN: Replaces binary cross-entropy with least squares loss, reducing vanishing gradients.
2. Regularization Techniques:
Gradient Penalty: Enforces Lipschitz constraint for smoother discriminator behavior.
Spectral Normalization: Controls weight matrix norms to stabilize discriminator training.
3. Training Techniques:
One-Sided Label Smoothing: Softens real labels to prevent discriminator overconfidence.
Balanced Training: Careful alternation of generator and discriminator updates to maintain equilibrium.
Mini-batch Discrimination: Helps detect diversity and improve generator outputs.
4. Architectural Innovations:
Using progressive growing of GANs to generate high-resolution images gradually.
Incorporating self-attention and multi-scale discriminators for better feature extraction.
