Stacked generative adversarial networks
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Stacked generative adversarial networks. / Huang, Xun; Li, Yixuan; Poursaeed, Omid; Hopcroft, John; Belongie, Serge.
In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 06.11.2017, p. 1866-1875.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Stacked generative adversarial networks
AU - Huang, Xun
AU - Li, Yixuan
AU - Poursaeed, Omid
AU - Hopcroft, John
AU - Belongie, Serge
N1 - Publisher Copyright: © 2017 IEEE.
PY - 2017/11/6
Y1 - 2017/11/6
N2 - In this paper, we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a bottom-up discriminative network. Our model consists of a top-down stack of GANs, each learned to generate lower-level representations conditioned on higher-level representations. A representation discriminator is introduced at each feature hierarchy to encourage the representation manifold of the generator to align with that of the bottom-up discriminative network, leveraging the powerful discriminative representations to guide the generative model. In addition, we introduce a conditional loss that encourages the use of conditional information from the layer above, and a novel entropy loss that maximizes a variational lower bound on the conditional entropy of generator outputs. We first train each stack independently, and then train the whole model end-to-end. Unlike the original GAN that uses a single noise vector to represent all the variations, our SGAN decomposes variations into multiple levels and gradually resolves uncertainties in the top-down generative process. Based on visual inspection, Inception scores and visual Turing test, we demonstrate that SGAN is able to generate images of much higher quality than GANs without stacking.
AB - In this paper, we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a bottom-up discriminative network. Our model consists of a top-down stack of GANs, each learned to generate lower-level representations conditioned on higher-level representations. A representation discriminator is introduced at each feature hierarchy to encourage the representation manifold of the generator to align with that of the bottom-up discriminative network, leveraging the powerful discriminative representations to guide the generative model. In addition, we introduce a conditional loss that encourages the use of conditional information from the layer above, and a novel entropy loss that maximizes a variational lower bound on the conditional entropy of generator outputs. We first train each stack independently, and then train the whole model end-to-end. Unlike the original GAN that uses a single noise vector to represent all the variations, our SGAN decomposes variations into multiple levels and gradually resolves uncertainties in the top-down generative process. Based on visual inspection, Inception scores and visual Turing test, we demonstrate that SGAN is able to generate images of much higher quality than GANs without stacking.
UR - http://www.scopus.com/inward/record.url?scp=85041903901&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2017.202
DO - 10.1109/CVPR.2017.202
M3 - Conference article
AN - SCOPUS:85041903901
SP - 1866
EP - 1875
JO - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
JF - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
T2 - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Y2 - 21 July 2017 through 26 July 2017
ER -
ID: 301826993