Editing Generative Adversarial Networks (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Designing a GAN-based image synthesis system:

'''1. Architecture selection by task'''
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Task classification:
├── Unconditional image synthesis → StyleGAN3
├── Class-conditional generation → BigGAN, StyleGAN-XL
├── Text-to-image (GAN-based) → GigaGAN
├── Paired image translation → Pix2Pix (paired data)
├── Unpaired image translation → CycleGAN (no paired data)
├── Super resolution → ESRGAN
└── Video generation → MoCoGAN, DIGAN
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'''2. Training stabilization recipe (for custom GAN)'''
* Use WGAN-GP or StyleGAN's R1 gradient penalty loss
* Spectral normalization on discriminator weights
* LeakyReLU (0.2) in discriminator; ReLU in generator
* Adam optimizer with β1=0.5, β2=0.999; learning rate 1e-4 to 2e-4
* Exponential moving average (EMA) of generator weights for smoother evaluation
* Progressive growing or batch size ramp-up for high-resolution targets

'''3. Data preparation'''
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Collect dataset (minimum 10k images; 100k+ for high quality)
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Crop + align (for face generation: align to landmarks)
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Resize to target resolution (power of 2: 64, 128, 256, 512, 1024)
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Normalize to [-1, 1] (matches Tanh output activation)
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[Optional] ADA (Adaptive Discriminator Augmentation) for small datasets
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'''4. Monitoring training health'''
* Plot G loss and D loss separately; they should remain in rough balance
* Sample fixed noise vectors (z_fixed) each epoch → visualize how G evolves
* Compute FID every 5k–10k iterations on 10k samples
* Alert if G loss spikes dramatically (mode collapse indicator)

[[Category:Artificial Intelligence]]
[[Category:Deep Learning]]
[[Category:Generative AI]]
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