From GAN to WGAN
📰 Lilian Weng's Blog
Wasserstein GAN improves training of generative adversarial networks by using a smooth metric to measure distance between probability distributions
Action Steps
- Understand the maths behind GAN models
- Identify challenges in training GANs
- Learn about Wasserstein GAN and its smooth metric
- Implement WGAN to improve model training
Who Needs to Know This
Machine learning researchers and engineers on a team can benefit from understanding the maths behind GANs and WGANs to improve model training and performance
Key Insight
💡 Wasserstein GAN adopts a smooth metric to measure distance between probability distributions, improving training
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💡 WGAN improves GAN training with smooth metric
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