Papers › LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation
LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation
Jianwei Yang, Anitha Kannan, Dhruv Batra, Devi Parikh
We present LR-GAN: an adversarial image generation model which takes scene structure and context into account. Unlike previous generative adversarial networks (GANs), the proposed GAN learns to generate image background and foregrounds separately and recursively, and stitch the foregrounds on the background in a contextually relevant manner to produce a complete natural image. For each foreground, the model learns to generate its appearance, shape and pose. The whole model is unsupervised, and is trained in an end-to-end manner with gradient descent methods. The experiments demonstrate that LR-GAN can generate more natural images with objects that are more human recognizable than DCGAN.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Conditional Image Generation | CIFAR-10 | LR-GAN | Inception score | 7.17 | #22 of 25 | Archive leaderboard | report |
| Image Generation | CUB 128 x 128 | LR-GAN | FID | 34.91 | #4 of 4 | Archive leaderboard | report |
| Image Generation | CUB 128 x 128 | LR-GAN | Inception score | 13.50 | #4 of 4 | Archive leaderboard | report |
| Image Generation | Stanford Cars | LR-GAN | FID | 88.80 | #4 of 4 | Archive leaderboard | report |
| Image Generation | Stanford Cars | LR-GAN | Inception score | 5.25 | #4 of 4 | Archive leaderboard | report |
| Image Generation | Stanford Dogs | LR-GAN | FID | 54.91 | #4 of 4 | Archive leaderboard | report |
| Image Generation | Stanford Dogs | LR-GAN | Inception score | 10.22 | #4 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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