Generative Adversarial Network (GAN )

Trade-Off: Fidelity vs Accuracy

ex: generating images and creating an art 

# unconditional generator from StyleGAN

np.random.RandomState(100)

batch_size = 4

truncation = 0.95 # condition: increase diversity/ decrease fidelity

# unconditional generator from StyleGAN


np.random.RandomState(100)

batch_size = 4

truncation = 0.05 # condition: decrease diversity/ increase fidelity

Text to image GAN (the simple DALL-E script) 

# this is an implementation of CLIP (Contrastive Language-Image Pre-Training)(paper)(2021) with Taming Transformers for High-Resolution Image Synthesis (paper)(2020)


alpha = 1 # the importance of the <include> input

include = ['A horse on the plane'] # place for your input


# Note: This illustrates the concept; its unpolished appearance is due to insufficient training.

Generating Magnetic Domain Pattern Images Using GANs

The aim is to extract parameters from images and/or generate new images of magnetization patterns using micromagnetic simulation data. 

Here, I show the images of simulated magnetization patterns generated using Generative Adversarial Networks (GANs). It shows the capability of GANs to generate magnetic domain pattern images.  

Midjourney AI (Illustrations for Neuromorphic Computing) 

I've been working with Modjourney AI to create some illustrations on neuromorphic computing. If you're interested, feel free to download them!