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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Citations
Cites
Deep Residual Learning for Image Recognition
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Auto-Encoding Variational Bayes
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Conditional Generative Adversarial Nets
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Improved Techniques for Training GANs
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Context Encoders: Feature Learning by Inpainting
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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
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Energy-based Generative Adversarial Network
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“Independent of our work, [4] proposes to regularize EBGAN [68] with entropy maximization in order to prevent the discriminator from degenerating to uniform prediction.”
BackgroundMethodCNN Features off-the-shelf: an Astounding Baseline for Recognition
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Cross Modal Distillation for Supervision Transfer
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“[51, 20] use the intermediate representations of one pre-trained DNN to guide another DNN in the context of knowledge transfer.”
BackgroundMethodFrom neural PCA to deep unsupervised learning
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Cited by
Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017) · arXiv · doi:10.48550/arxiv.1704.00028 · Cited by 6,800
“Least squares generative adversarial networks,”
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Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization
Huang, X., & Belongie, S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.167 · Cited by 5,076
Self-citationProgressive Growing of GANs for Improved Quality, Stability, and Variation
Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2017) · arXiv · doi:10.48550/arxiv.1710.10196 · Cited by 4,727
Generative Adversarial Networks: An Overview
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2765202 · Cited by 4,540
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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