ECE 599/692 Deep Learning. Lecture 12 GAN - Introduction
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1 ECE 599/692 Deep Learning Lecture 12 AN - Introduction Hairong Qi, onzalez Family Professor Electrical Engineering and Computer Science University of Tennessee, Knoxville hqi@utk.edu 1 AN Two neural networks compete against each other A generator network : mimic training samples to fool the discriminator A discriminator network D: discriminate training samples and generated samples Training samples x~q(x) D Real/fake? enerated samples x~p(x z) D x : x~q x? (z) Noise z~p(z) For D: For : max D min E x~q(x) log D(x) E z~p(z) log 1 D((z)) + E z~p(z) log 1 D((z)) 2 day month year documentname/initials 1
2 AN The objective function of ANs: min max D E x~q(x) log D(x) x + E z~p(z) log 1 D((z)) Feedforward Backpropagation Real? z D Fake? x' Real? 3 AN - Drawbacks Mode missing problem enerate unrealistic images Hard to learn to generate discrete data, e.g., text 3/22/ day month year documentname/initials 2
3 AN-based Image Manipulation Summary y x Real? x E z D Fake? x' Seldom use original AN Concatenate an encoder to Concatenate extra feature to z 5 Evolution of AN 2014 AN [NIPS] Laplacian Pyramid [NIPS] DCAN [ICLR] InfoAN [NIPS] RNN+AN [ICML] VAE+AN [ICML] Super-Resolution [ECCV] [CVPR] 2017 Latent-Manipulation [ECCV] [CVPR] Domain transformation [ICML] [CVPR] CoAN [NIPS] AAE [ICLR] CatAN [ICLR] Born Fermenting Booming of Improvements and Applications: Higher resolution Flexible manipulation Instability [ICLR] Mode Missing [ICLR] Theory: Drawbacks & Solutions 6 day month year documentname/initials 3
4 Conditional Adversarial Autoencoder for Age Progression/Regression Zhifei Zhang, Yang Song, Hairong Qi, Conditional adversarial autoencoder for age progression/regression, CVPR, Motivation If I provide you a face image of mine (without telling you the actual age when I took the picture) and a large amount of face images that I crawled (containing labeled faces of different ages but not necessarily paired), can you show me what I would look like when I am 80 or what I was like when I was 5? Younger? Younger? Older? Older? 8 day month year documentname/initials 4
5 Age Progression/Regression Regression/Rejuvenation iven face Progression/Aging years old Image manipulation conditioned on personality and age 9 Existing Works 5 years old T 0 T 1 T 3 Query Label: 10 roup-wised learning Query with label Step-to-step transition 10 day month year documentname/initials 5
6 Our Work Query Existing works: roup-wised learning Query with label Step-to-step transition Label: None Our work: Joint learning Query without label One-step transition 11 Traversing on the Manifold Latent space x 1 E Personality (z) [z 2, l 2 ] [z 1, l 1 ] x 2 x 1 M Age (l) x 2 Label Uniform D z Real D img 9/9/ noise faces 12 day month year documentname/initials 6
7 Conditional Adversarial Autoencoder - CAAE 128x128x3 64x64x64 32x32x128 8x8x512 16x16x x8x x16x512 32x32x256 64x64x x128x64 enerator 128x128x3 Input face Conv_1 Encoder E Conv_2 Conv_3 Conv_4 FC_1 Reshape z l Reshape Deconv_1 FC_2 Deconv_2 Deconv_3 Deconv_4 Output face 1x1xn L 2 loss z or p(z) 64 Discriminator on z -- D z Prior distribution of z (uniform) FC_3 FC_4 FC_2 FC_1 Label Resize to 64x64x10 128x128x3 Input/ output face 64x64x(n+16) Conv_1 32x32x32 Discriminator on face image -- D img 16x16x64 8x8x FC_2 Conv_3 Conv_4 FC_1 Conv_2 Reshape Figure 3. Structure of the proposed CAAE network for age progression/regression. The encoder maps the input face to a vector 13 CAAE D on z D on image 14 day month year documentname/initials 7
8 CAAE - Evaluation Input 15 CAAE - Evaluation Qualitative Comparison No existing work reported regression/rejuvenation results 16 day month year documentname/initials 8
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