This document discusses leveraging machine intelligence to improve sensory experiences through augmenting human perception with AI. It provides background on the state of AI, highlighting breakthroughs in deep learning that enabled tasks like image generation and style transfer using neural networks. The author proposes an automated image editing platform with an API and app for applying AI-powered, photorealistic image filters to any photos in bulk, minimizing human effort for content creation while producing high-quality visuals.

1. Leveraging machine intelligence
to improve sensory experiences
Dr Esin Yavuz, Co-founder & Chief Scientist
MI Garage Meetup: “How can the creative industries use AI?”
7 November 2018

2. Augmenting sensory perception
with machine intelligence
Linking machine intelligence and human perception requires
real-time and automated image processing. To enable this, we
are building a platform that allows fast and automated
processing of images using AI.

3. Outline
●
Background & State of the art
●
AI for photorealistic image transformations
●
Automating image editing

4. What is AI?
●
“Science of making machines do things that would require intelligence if done by men.”
Minsky
●
Often used to refer to statistical methods that aim to fnd structure and regularities in data
●
Used for making deductions based on observations
●
Applied to problem solving, reasoning, natural language processing, machine perception

6. ●
2006: NVIDIA releases CUDA for general purpose GPU
computing
High-performance computing becomes easier to use
●
2010: ImageNet Large Scale Visual Recognition challenge
-Classifcation task based on a dataset of 10M images in
10K+ classes
Importance of number of examples and problem defnition
●
2012: Alexnet achieves top-5 error of 15.3%, more than 10.8
percentage points lower than that of the runner up on
ImageNet competition using a deep convolutional network
Beginning of the Deep Learning era
Breakthroughs in the last decade

7. https://distill.pub/2017/feature-visualization/

8. Deep Dream (Google) 2015
●
Initially designed as a tool to understand internal properties of neural networks
●
Whatever the network sees, it adjust the other pixels to see more of it
●
Allows creative image manipulations based on internal structure of a neural network

9. Neural Style Transfer – Gatys et al. 2016
Combining the
content of an image
with the style of
another image

10. https://deepart.io/

11. https://prisma-ai.com/

12. Image generation with Generative Adversarial Nets
(GAN) – Goodfellow et al. 2014

13. More recent GAN examples
Brock et al - BigGAN (Deepmind) 2018Karras et al. (NVIDIA) 2017
Evolving to be a new tool for artistic creation

16. Style Transfer with CycleGAN

17. Artistic style transfer with GAN-based methods
Drawbacks
●
While artistic style transfer can
be easily achieved, photorealistic
transformations are not easy
●
Mostly support low-res, if not
they need massive computational
power
●
Diffcult to generalise to different
content domains

18. Limitations of CycleGAN

19. Why do we need photorealistic transformations?
●
Good quality visual content is indispensable for a better
communication through visuals. However, creating good quality
visuals needs expertise and it is time consuming.
●
Existing tools for image flters provide synthetic results that look
similar, most are slow, do not scale for bulk image processing, and
still require substantial user expertise.
●
Apart from content creation, there is a need in other felds such as
data augmentation, video game development, rendering, enhancing
video streams.

20. Problems of applying AI to casual photography
Flickr/Mr Boss
Flickr/Yuma Hori
Flickr/Bex Walton

21. AI filers lhal work witlh any l pe of itmages
Transform any type of image
to refect different daytime
lighting conditions

22. AI-powered itmage filers

23. AI-powered itmage filers

24. AI-powered itmage filers
More examples here: cyanapse.com/ai-tech/photorealistic-image-flters

25. ●
Use AI-powered photorealistic flters
that work with any type of photos
●
Automate dull image editing tasks
●
Enable bulk image editing with
custom pipelines
Automated Image Editing Platform
1- Back-end API for fast and automated image processing
2- Front-end App for user-friendly interface

26. ●
RESTful API with intuitive
endpoints
●
Bulk processing that scales
to users’ needs
●
User-defned image editing
pipelines
●
Includes AI-enhanced
image styling flters
Automating image editing

27. Front-end
●
Mobile and web app to try out
AI flters
●
Use AI photo flters in just a
few clicks
●
Instantaneous generation of
various previews
●
Less than a second to
enhance 4k-res images, 40
ms for a low-res image

28. Advantages
●
4k resolution images are enhanced in less than a second
●
AI transformations run in the cloud (fexible and scalable)
●
We provide image enhancements that are photorealistic and that work for
every type of photos
●
Minimise the time spent on creating good quality content, allowing content
creators to focus on more important tasks
●
Enable real-time and photorealistic enhancements of video streams with
minimal human intervention
●
Generate multiple possible transformations to help content creators
preview the results beforehand, and to pass their messages better

29. Thank you!
Email: info@cyanapse.com
Register here:
cyanapse.com/beta-testing
@cyanapse