The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Stacked Generative Adversarial Networks
Open accessAbstract
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.
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Cites
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Cited by
Improved training of Wasserstein GANs
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“Least squares generative adversarial networks,”
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Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization
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Self-citationProgressive Growing of GANs for Improved Quality, Stability, and Variation
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Generative Adversarial Networks: An Overview
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StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation
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StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks
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Self-citationA Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
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Pros and cons of GAN evaluation measures
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$L1$ -Norm Batch Normalization for Efficient Training of Deep Neural Networks
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Wasserstein Introspective Neural Networks
Lee, K., Xu, W., Fan, F., & Tu, Z. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00390 · Cited by 56
Metadata
- Authors
- Huang, X., Li, Y., Poursaeed, O., Hopcroft, J., & Belongie, S.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 1866-1875
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2566832195
- Cited by
- 434
- References
- 16
- FWCI
- 22.08
- Type
- conference paper
- Language
- en
- Open access
- green
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