Methods › Computer Vision › Generative Models › HDCGAN
High-resolution Deep Convolutional Generative Adversarial Networks
HDCGAN
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
HDCGAN, or High-resolution Deep Convolutional Generative Adversarial Networks, is a DCGAN based architecture that achieves high-resolution image generation through the proper use of SELU activations. Glasses, a mechanism to arbitrarily improve the final GAN generated results by enlarging the input size by a telescope ζ is also set forth.
A video showing the training procedure on CelebA-hq can be found here.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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High-Resolution Deep Convolutional Generative Adversarial Networks 17 Nov 2017 · 1 repository · arXiv:1711.06491
Tasks archive 2025-07-28
5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| GPU | 1 |
| Image Generation | 1 |
| MS-SSIM | 1 |
| SSIM | 1 |
| Vocal Bursts Intensity Prediction | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections