To address sample collapse in WGANs when generating high-quality images from low-resolution inputs, you can follow the following key points:
- Improved Weight Clipping: In WGAN, weight clipping can cause instability. Instead, use gradient penalty to enforce the Lipschitz constraint more smoothly.
- Multi-Scale Generators: Use multi-scale architectures (e.g., progressive growing) to generate high-resolution images from low-resolution inputs.
- Feature Matching Loss: Add a feature matching loss to encourage the generator to match statistics of real and generated images at different layers of the discriminator.
- Two-Stage Training: First, train the generator on low-resolution data and then progressively refine it with higher-resolution data.
Here is the code snippet you can refer to:
In the above code, we are using the following key points:
- Gradient Penalty: Replaces weight clipping to enforce the Lipschitz constraint without instability.
- Multi-Scale Architecture: This can be introduced in the generator to refine outputs progressively.
- Feature Matching: Helps improve quality by matching real and generated feature statistics.
- Two-Stage Training: Start with low-resolution data and gradually increase resolution to avoid collapse.
Hence, by referring to the above, you can address sample collapse in WGANs when generating high-quality images from low-resolution inputs.
