Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Deep Learning Image Manipulation
Search
Leszek Rybicki
May 18, 2017
Research
2
190
Deep Learning Image Manipulation
Illustrated guide to some image manipulation methods, with demonstration.
Leszek Rybicki
May 18, 2017
Tweet
Share
More Decks by Leszek Rybicki
See All by Leszek Rybicki
Let's talk about Fakes
lunardog
0
96
How to Patch Image Classifiers
lunardog
0
1.7k
Towards Realistic Predictors - EN
lunardog
0
1.6k
Towards Realistic Predictors
lunardog
1
2k
Deep Learning Hot Dog Detector
lunardog
0
230
Finding beans in burgers: paper reading notes
lunardog
0
1.2k
Kelner: Serve Your Models
lunardog
0
100
Image Analysis at Cookpad
lunardog
1
1.5k
Kelner: serve your models
lunardog
1
330
Other Decks in Research
See All in Research
Weekly AI Agents News! 9月号 プロダクト/ニュースのアーカイブ
masatoto
1
110
Inside Phishing Groups: Trust No One
0x1shu
0
110
12
0325
0
110
第60回名古屋CV・PRMU勉強会:CVPR2024論文紹介(Vision Transformer)
waka_90b
1
200
Practical The One Person Framework
asonas
1
1.4k
Tietovuoto Social Design Agency (SDA) -trollitehtaasta
hponka
0
2.1k
20240725異文化融合研究セミナーiSeminar
tadook
0
150
「Goトレ」のご紹介
smartfukushilab1
0
740
熊本から日本の都市交通政策を立て直す~「車1割削減、渋滞半減、公共交通2倍」の実現へ~@公共交通マーケティング研究会リスタートセミナー
trafficbrain
0
120
秘伝:脆弱性診断をうまく活用してセキュリティを確保するには
okdt
PRO
3
730
尺度開発における質的研究アプローチ(自主企画シンポジウム7:認知行動療法における尺度開発のこれから)
litalicolab
0
320
[CV勉強会@関東 CVPR2024] Visual Layout Composer: Image-Vector Dual Diffusion Model for Design Layout Generation / kantocv 61th CVPR 2024
shunk031
1
410
Featured
See All Featured
Large-scale JavaScript Application Architecture
addyosmani
510
110k
KATA
mclloyd
29
13k
GraphQLとの向き合い方2022年版
quramy
43
13k
Save Time (by Creating Custom Rails Generators)
garrettdimon
PRO
27
790
Fashionably flexible responsive web design (full day workshop)
malarkey
404
65k
Unsuck your backbone
ammeep
668
57k
Performance Is Good for Brains [We Love Speed 2024]
tammyeverts
3
370
Rails Girls Zürich Keynote
gr2m
93
13k
Intergalactic Javascript Robots from Outer Space
tanoku
268
27k
How To Stay Up To Date on Web Technology
chriscoyier
788
250k
Distributed Sagas: A Protocol for Coordinating Microservices
caitiem20
328
21k
[RailsConf 2023] Rails as a piece of cake
palkan
51
4.9k
Transcript
%FFQ-FBSOJOH *NBHF.BOJQVMBUJPO BOJMMVTUSBUFEHVJEF .-,JUDIFO
"CPVUNF w -FT[FL3ZCJDLJ w HJUIVC!MVOBSEPH w CPSOJO1PMBOE w .-3FTFBSDIFSBU$PPLQBE w
*MJLFOBUUP
DBSFFST!DPPLQBEDPN 8BOUUPXPSLXJUIVT
$POWPMVUJPOBM "SJUINFUJD OCIKE
*NBHFTUPGFBUVSFT
$POWPMVUJPO http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html input output input output kernel
4USJEF http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html 2px 2px 2px 2px
1BEEJOH http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html 2px 2px
4USJEF QBEEJOH http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html
5SBOTQPTFE http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html simulated here with padding also called “deconvolution” “fractional
stride”
%PXOTBNQMJOH features or small resolution image convolutional layer or layers
RGB image input output
6QTBNQMJOH upsampling CNN layer or layers RGB image features or
small resolution image input output
&ODPEFS%FDPEFS D E image in Decoder Encoder image out feature
space
'VMMZ$POOFDUFE $MBTTJpFS approve loan reject class data or features also
called “Dense” layer
$//$MBTTJpFS food person plant other AlexNet, LeNet, VGG…
'PPE/FU ™ food not food
@teenybiscuit
None
@teenybiscuit
@teenybiscuit
@teenybiscuit
@teenybiscuit
@teenybiscuit
(FOFSBUJWF "EWFSTBSJBM /FUXPSLT
Generator Discriminator https://speakerdeck.com/lunardog/deep-convolutional-voight-kampf-test “Couple of bots studying for the Turing
Test”
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Alec
Radford, Luke Metz, Soumith Chintala (Submitted on 19 Nov 2015 (v1), last revised 7 Jan 2016 (this version, v2)) https://arxiv.org/abs/1511.06434
Generator Discriminator G MPPLTMFHJU UPUBMMZTIPQQFE D
G SFBM GBLF D D(G(noise)) ˠ real (FOFSBUPSUSBJOJOH Discriminator acts
as the teacher
G SFBM GBLF D SFBM GBLF D D(G(noise)) ˠ fake
D(photo) ˠ real %JTDSJNJOBUPSUSBJOJOH Generator provides negative examples
None
https://www.youtube.com/watch?v=rs3aI7bACGc ©Yota Ishida
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Alec
Radford, Luke Metz, Soumith Chintala (Submitted on 19 Nov 2015 (v1), last revised 7 Jan 2016 (this version, v2)) https://arxiv.org/abs/1511.06434
$POEJUJPOBM ("/T
G NBMF GFNBMF DIJME FMEFSMZ G(noise | conditions) $POEJUJPOBM(FOFSBUPS
SJHIU XSPOH NBMF GFNBMF DIJME FMEFSMZ D $POEJUJPOBM%JTDSJNJOBUPS
SJHIU XSPOH NBMF GFNBMF DIJME FMEFSMZ D SJHIU XSPOH NBMF
GFNBMF DIJME FMEFSMZ SJHIU XSPOH NBMF GFNBMF DIJME FMEFSMZ D D
SJHIU XSPOH D $POEJUJPOBM("/ https://arxiv.org/abs/1411.1784 Conditional Generative Adversarial Nets Mehdi
Mirza, Simon Osindero (Submitted on 6 Nov 2014) Generator Discriminator NBMF GFNBMF DIJME FMEFSMZ G NBMF GFNBMF DIJME FMEFSMZ same condition
G NBMF GFNBMF DIJME FMEFSMZ SJHIU XSPOH NBMF GFNBMF DIJME
FMEFSMZ D $POEJUJPOBM("/ Discriminator Generator
https://www.faceapp.com/ Disclaimer: FaceApp authors don’t disclose their method. This is
only my guess. It may have nothing to do with GANs. original
original https://www.faceapp.com/
https://www.faceapp.com/ original
"SUJTUJD4UZMF5SBOTGFS Improved!
https://prisma-ai.com/
https://prisma-ai.com/ https://prisma-ai.com/
https://prisma-ai.com/ https://prisma-ai.com/
https://prisma-ai.com/ https://prisma-ai.com/
https://arxiv.org/abs/1603.08155 transformation network loss network Gram matrices in feature space
pre-trained content image style image
“Gram matrices in feature space” https://en.wikipedia.org/wiki/Gramian_matrix
https://www.youtube.com/watch?v=xVJwwWQlQ1o
$ZDMF("/
https://github.com/junyanz/CycleGAN
https://github.com/junyanz/CycleGAN
https://github.com/junyanz/CycleGAN
(FOFSBUPS transformation network https://arxiv.org/abs/1603.08155
GBLF IPSTF GBLF IPSTF … %JTDSJNJOBUPS fully convolutional judges patches
of the input image https://arxiv.org/abs/1603.08155
"EWFSTBSJBM-PTT X F G Y GBLF [FCSB GBLF [FCSB …
GBLF IPSTF GBLF IPSTF … X(F(horse)) ˠ classify as zebra Y(F(zebra)) ˠ classify as horse
$ZDMF-PTT G F G(F(image))ˠ the same image F G F(G(image))ˠ
the same image
https://www.youtube.com/watch?v=9reHvktowLY
5IF&OE