Draft4 of Insight Presentation

1. Deep Convolutional Generative Adversarial Networks (DCGANs) for Creating Pixel Art
By Lawrence Du

2. PROBLEM: Creating art for mobile apps takes skill and money.
● Stickers are big business
○ LINE messenger - a chat app popular
in Asia made over $250 million dollars
from stickers in 2015
○ Facebook, Snapchat, and Twitter have
augmented their apps with sticker
marketplaces
● Artwork can easily meet or exceed the cost
of programming for many games today
● In Asia, communicating with sticker art can
be easier than writing text on mobile
devices

4. Solution: Use Deep Learning for AI assisted art generation
Fake pictures of bedrooms

5. Solution: Use Deep Learning for AI assisted art generation
AI generated music album covers

6. An A.I. assistant for creating art

7. Implementation
DISCRIMINATOR
GENERATOR

8. Network Balancing
DISCRIMINATOR
LOSS
GENERATOR
LOSS

9. Using Pokémon as a training set
● Images of 722 Pokémon available
● 76,469 animation frames from the most recent generation (XY)
● Feature engineering: 19231 frames from 231 Pokémon selected
for consistent naturalistic morphology.

10. Using Pokémon as a training set
● Images of 722 Pokémon available
● 76,469 animation frames from the most recent generation (XY)
● Feature engineering: 19231 frames from 231 Pokémon selected
for consistent naturalistic morphology.

11. Implementation
● Wrote DCGAN using
● 30+ neural architecture combinations tested.
● Expanded training set size by random application of
brightness, hue, contrast, and left-right transformations.
● Training takes 20-30 minutes on Geforce GTX 1060 GPU
● Python Flask

13. Lawrence Du
larrydu88@gmail.com
PhD Biological Sciences (UC San Diego)