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Howhot.ioDeepLearning:fromtheorytovirality
MachineLearningMeetup,GoogleZurich
RasmusRotheFeb9,2016
2slideintroducDon
16-18SEPTEMBER2016
Howhot.io
AlexZimmermann
JanBerchtold
RasmusRothe
RaduTimoQe
LucVanGool
howhot.io
hTps://www.instagram.com/explore/tags/howhot/
Brazil
Germany
India
Iran
Netherlands
Russia
Switzerland
Turkey
UnitedKingdom
UnitedStates
1.Iran(2,436,946)
2.UnitedStates(1,465,615)
3.Germany(686,393)
4.Russia(372,782)
5.Turkey(333,366)
6.UnitedKingdom(254,328)
7.Switzerland(252,712)
8.India(236,275)
9.Netherlands(226,580)
10.Brazil(222,884)
229countries!
It’smorethanajoke…• RasmusRothe,RaduTimoQe,andLucVanGool."DEX:Deep
EXpectaDonofapparentagefromasingleimage.”,ICCVWorkshops.2015.– WinnerofLAPchallengeonapparentageesDmaDon(115teams)– NVIDIAChaLearnLAP2015BestPaperAward
• RasmusRothe,RaduTimoQe,andLucVanGool."Somelikeithot-visualguidanceforpreferencepredicDon."arXivpreprintarXiv:1510.07867(2015),insubmission
• RasmusRothe,RaduTimoQe,andLucVanGool.“DeepexpectaDonofrealandapparentagefromasingleimagewithoutfaciallandmarks”,insubmission
• IMDB-WIKIdatasetpubliclyavailable: hTps://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/
120,000profileimages22millionhot-or-notraDngs
44.81%ofmen’sraDngsareposiDve
8.58%ofwomen’sraDngsareposiDve
Preferencebyage
?
Whatwetrytopredict
DeepLearning
PredicDonRatersVisualspace
Nearestneighbor
CollaboraDvefiltering
PTQ
4.08.42.59.79.13.27.77.66.01.50.58.76.00.82.58.50.88.28.93.88.18.21.87.0
6.73.97.40.80.85.43.93.8
8.21.87.06.73.97.45.94.0
Visual
Query
Pastra
Dngs
OR
MatrixfactorizaDon
Latentspace(Q)
Regression
Ourpipeline
QuanDtaDveresults
Preferencesbyclusters
ExperimentsonMovieLensdataset
howhot.ioexplainedin3slides
1)DatatolearnfromDatasource #Photos Gender
(m/f)Age
(23.4yrs)A8rac9veness(top23%)IMDb
500,000 x x
Wikipedia
60,000 x x
Blinq
120,000 x x x
2)Teachingthedeepneuralnetwork
1.Inputimage2.Facedetec2on3.Croppedface4.Featureextrac2on5.Predic2on
Mathiasetal.detector +40%margin VGG-16architectureSoHmaxexpectedvalue Σ = 23.4years
0
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0.04
0.06
0.95
1.43
1.73
2.11
...
0.84
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1.22
1.94
1.85
0.54
0.92
=
1.Inputimage2.Facedetec2on3.Croppedface4.Featureextrac2on5.Predic2on
Mathiasetal.detector +40%margin VGG-16architectureSoHmaxexpectedvalue Σ = 70%aKrac2ve
0
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99
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*
0.04
0.06
0.95
1.43
1.73
2.11
...
0.84
...
1.22
1.94
1.85
0.54
0.92
=
3)Whathappenswhenyouuploadaphoto
ApplicaDonsofhowhot.iotechnology
