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Deep Learning Image Manipulation

Deep Learning Image Manipulation

Illustrated guide to some image manipulation methods, with demonstration.

Leszek Rybicki

May 18, 2017
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  1. 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
  2. G SFBM GBLF D SFBM GBLF D D(G(noise)) ˠ fake

    D(photo) ˠ real %JTDSJNJOBUPSUSBJOJOH Generator provides negative examples
  3. 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
  4. SJHIU XSPOH NBMF GFNBMF DIJME FMEFSMZ D SJHIU XSPOH NBMF

    GFNBMF DIJME FMEFSMZ SJHIU XSPOH NBMF GFNBMF DIJME FMEFSMZ D D
  5. 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
  6. G NBMF GFNBMF DIJME FMEFSMZ SJHIU XSPOH NBMF GFNBMF DIJME

    FMEFSMZ D $POEJUJPOBM("/ Discriminator Generator
  7. 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
  8. GBLF IPSTF GBLF IPSTF … %JTDSJNJOBUPS fully convolutional judges patches

    
 of the input image https://arxiv.org/abs/1603.08155
  9. "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