Seaweed GAN
Generating dried-seaweed sheets with a DCGAN.
The problem
I wanted to see how well a GAN could synthesize a very texture-heavy, niche subject — dried-seaweed (gim) sheets — and learn DCGAN training hands-on.
What it does
Trains a DCGAN: a generator turns random noise into seaweed-sheet textures while a discriminator learns to tell real sheets from generated ones; the two compete until the fakes look convincing. Produces 512×512 and upscaled 1600×1600 samples.
Stack
- Python
- TensorFlow
- Keras
- DCGAN
- NumPy
- Matplotlib
My role
- Generator / discriminator design
- Training loop
- Sample generation
Highlights
Adversarial training
The generator learns to fool a discriminator that learns to spot fakes — converging toward realistic seaweed textures.
Texture-faithful output
Captures the speckled, fibrous look of real gim sheets, not just blurry blobs.