Lviv IT Arena is a conference specially designed for programmers, designers, developers, top managers, inverstors, entrepreneurs and startuppers. Annually it takes place at the beginning of October in Lviv at Arena Lviv stadium. In 2016 the conference gathered more than 1800 participants and over 100 speakers from companies like Microsoft, Philips, Twitter, UBER and IBM. More details about the conference at itarena.lviv.ua.

1. Applied
Deep-Learning
ComputerVision: Landscape,Capabilities and Case-studies
Programming is an art
where artist understands
little what was created.
Unknown Author

2. A.I. headlines
Google DeepMind software masters the game
ofGo, takes aim at the world’s top player
GeekWire, January, 2016
A Learning Advance in Artificial Intelligence
Rivals Human Abilities
NewYork Times, December, 2015
AI is nearly as good as humans in detecting
breast cancer.
Engadget, June, 2016

3. Why it is important?
Speech
FinTex
ADAS
Medical
Social
Security
99,98%_______
10,000K
90%_____
50K

4. Why NOW?
Needs Ways
Hardware
Methods
Tools
Data
Use-Cases

5. Demand Pyramid
integration
classification
comprehension
Value

6. Challenge: Video Comprehension
✔Provide ranking for certain video event
• TecVID MED’13 – 16 (Audio, OCR, Speech)
✔Assign action label to video event
• Dense trajectory features [Wang, 13]
• CNN features for optical flow [Simonyan, 14]
• 3D convolution networks for videos [Tran, 15]
✔Action localization
• Learning to track for spatial-temporal action
localization [Weinzapfel, 15]

7. Pros & Cons
Highly flexible
Adaptable to the new tasks
Great for complex noisy data
Deterministic latency
Undebuggable
Compute & power intensive
Large memory footprint
Not quite understood by developers

8. What’s different?

9. ILSVRC – Architectures Competition
ResNet
GoogleNet
VGG
AlexNet
28,2
25,8
16,4
11,7
6,7 7,3
3,57
2010 AlexNet 2011 AlexNet 2012 AlexNet 2013 AlexNet 2014 GoogleNet 2014 VGG 2015 ResNet
layers-> 152
2219
8 8
5,1
errors% ->

10. Dev Landscape: Frameworks
✔Caffee
✔TensorFlow
✔Torch & Co.

11. Dev Landscape: Tools
✔NVidia Digits
✔OpenML
✔Proprietary solutions

12. CNN Optimizations
✔Fine tuning – learning optimization
✔Nets pruning – less memory footprint
✔Forward speed – decrease latency

13. Architectures Performance
CNNs Alex
Net
Google
Net
VGG-19 VGG-16 SqueezeN
et
ResNet-
152
Leaning time, hours 187 673 4100 3500 n.a 2680
Forward, sec 0,3 1 4 3,5 0,3 ?
Weights, MB 230 51 548 528 4,7 230
Top-5 error, % 16,4 6,7 7,3 7,4 16,4 3,57
Number of layers 8 22 19 16 65 152

14. Machine Learning: Misconceptions
No self-learning
No universal architecture
Spatial-temporal analysis

15. Examples

16. AlexNet at ARM Mali T760
AlexNet@CAFFEE image visual
analysis. Creates tags and
understand your picture offline

17. DNN for ADAS
Driver assist systems leverage
DNNs to classify road
obstacles. Hybrid HOG + DNN

18. YOLO + DNN + Dense Trajectories
Specialized video surveillance
to achieve unprecedented
value for pharma. Provides
reliability and accuracy of
video data. Example of visual
cognition.

19. Lessons Learned:
Deep Learning is in it’s infancy
Be ready for chaotic tools landscape
Don’t break up with traditional
algorithmic approach
Be ready to change your mindset!

20. Thank you!
Questions?
Programming is an art
where artist understands
little what was created.
Unknown Author