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1
2017
Deep learning for aerospace applications
Alexandre Boulch
2
2017
Deep Learning
Lee Sedol 2015/10Ke Jie 2017/05
3
2017
Deep Learning
Personal assistantPersonalised learningRecommendationsRéponse automatique
Deep learning and Big data for
cardiology
4
2017
Deep Learning
5
2017
Overview
Machine Learning
Deep Learning
DeLTA
6
2017
AI
The science and engineering of making intelligent machines.
Logical, search, pattern recognition, planning, inference, learning from experience...
7
2017
AIIntelligent machines
Machine LearningLearning from experience
Model withparametersInput Output
Tuning parameters
Trainingdata
8
2017
Machine learning starts in the 60’s
9
2017
Overview
Machine Learning
Deep Learning
DeLTA
10
2017
Deep learning
11
2017
Deep learning
12
2017
Deep learning
DatasetsCompetitionEmulation
New optimizersNew neural layersNew architectures
...
BetterFaster Stronger
13
2017
Deep learning
2017
14
2017
Deep learning
AIIntelligent machines
Machine LearningLearning from experience
Deep learningAuto-learningDeep neural networks
15
2017
Deep learning
Data
Feature extraction Decision
Expert
Machine learning
Knowledge about data and application
Knowledge about statistics
16
2017
Deep learning: a massively data driven approach
Data
Features and decision
Data knowledge Statistics, optimization
Deep neural networks
Network suited for applications
17
2017
Deep learning: a massively data driven approach
Data
Features and decision
Data knowledge Statistics, optimization
Deep neural networks
Network suited for applications
18
2017
Machine learning at ONERA
DTIS
DAAA
DMAS
DMPE
DEMR DOTA
DPhiEE
19
2017
Deep learning at ONERA
DTIS
DAAA
DMAS
DMPE
DEMR DOTA
DPhiEE
A. Chan-Hon-Tong, S. Herbin, B. Le Saux, A. Boulch ...
20
2017
Semantic Map labeling
+
RG
BC
ompo
site
3 x
( 3x
3 c
onv.
+ R
eLU
)
Aerial images, multimodal (RGB, IR, DSM, ...)Fusion networksPhD Nicolas Audebert (nicolas.audebert.at)
21
2017
Point cloud labeling
2 x ( 3x3 conv. + ReLU )
Max Pooling 2x2
Max Pooling 2x2
Max Pooling 2x2
Max Pooling 2x2
2 x ( 3x3 conv. + ReLU )
3 x ( 3x3 conv. + ReLU )
3 x ( 3x3 conv. + ReLU )
Deconv. 3x3
Batch Norm2 x ( 3x3 conv. + ReLU )
Batch Norm3 x ( 3x3 conv. + ReLU )
Batch Norm3x(3x3 conv. + ReLU )
Batch Norm2 x ( 3x3 conv. + ReLU )
VGG 16
Deconv. 3x3
Deconv. 3x3
Deconv. 3x3
3 x ( 3x3 conv. + ReLU )
Conca
tenati
on
Leader on Semantic 8 LIDAR datasetTransfer to photogrammetryCode available online (DeLTA website)
22
2017
RGB and Depthfor persondetectionimprovment
PhD Joris Gueryjorisguerry.fr
Detection
23
2017
Detection in low resolutionImagesExploitation of imagesSequences for detection
Juliette Chataigner (Intern)
Detection
24
2017
Depth from defocus
Sensor specific processing Depth from de focus.PhD Macella Carvalho
25
2017
Zero Shot Learning
Zero Shot LearningLearning based on attributesPhD Maxime Bucher
Zero-Shot Learning via Visual AbstractionStanislaw Antol, Larry Zitnick, Devi Parikh
26
2017
Overview
Machine Learning
Deep Learning
DeLTA
27
2017
Deep learning for aerospace ONERA⇒
28
2017
Data
Frameworks
Development
R&D for aerospace and defense
Deep learning for aerospace ONERA⇒
29
2017
LE PRF DeLTA
Deep Lab
Applications
Core skills
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2017
Domain adaptation
New architectures
State of the art
Databases
Core skills
31
2017
LE PRF DeLTA
Software and help to solve problems with
machine learning
Tutorials
Code
Networks
Results
Datasets
Deep Lab
Academics and industrials
Improve ONERA research exposure
32
2017
Deep Lab
Generic code
Problem code
Network base
Networks Database code
Database
Validation
How To
Learning
Practical case
33
2017
DAAAFluid
mecanics
DTISRobotics
DMASMaterial
mecanics
DEMRDetection and
recognition
DOTAAtmospheric
Corr.
Deep Lab
Applications
Core skills
34
2017
TransferExperimentEvaluate
Move forwardInnovate
CapitalizeShareReproduce
Deep Lab
Applications
Core skills
4 year project
delta-onera.github.io
35
2017
“We chose it because we deal with huge amounts of data. Besides, it sounds really cool.”
Larry Page - Google