Applied Deep-Learning

Computer Vision: Landscape, Capabilities and Case-studies

Programming is an art where artist understands little what was created.

Unknown Author

A.I. headlines

Google DeepMind software masters the game of Go, 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

Why it is important?

Speech

FinTex

ADAS

Medical

Social

Security

99,98% _______ 10,000K

90% _____ 50K

Why NOW?

Needs Ways

Hardware

Methods

Tools Data

Use-Cases

Demand Pyramid

integration

classification

comprehension

Value

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]

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

What’s different?

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

22 19 8 8

5,1

errors% ->

Dev Landscape: Frameworks

✔Caffee

✔TensorFlow

✔Torch & Co.

Dev Landscape: Tools

✔NVidia Digits

✔OpenML

✔Proprietary solutions

CNN Optimizations

✔Fine tuning – learning optimization

✔Nets pruning – less memory footprint

✔Forward speed – decrease latency

Architectures Performance

CNNs Alex Net

Google Net

VGG-19 VGG-16 SqueezeNet

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

Machine Learning: Misconceptions

No self-learning

No universal architecture

Spatial-temporal analysis

Examples

AlexNet at ARM Mali T760

AlexNet@CAFFEE image visual analysis. Creates tags and understand your picture offline

DNN for ADAS

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

YOLO + DNN + Dense Trajectories

Specialized video surveillance to achieve unprecedented value for pharma. Provides reliability and accuracy of video data. Example of visual cognition.

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!

Thank you!

Questions?

Programming is an art where artist understands little what was created.

Unknown Author