Research map / Image generation / GANs
Constrained Generative Adversarial Networks for Interactive Image Generation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add relative-constraint conditioning and constraint-critic training to WGAN | WGAN · MNIST | mean constraint satisfaction error | Helped |
| Increase the input set from 1 to 10 constraints (ablation) | CONGAN · MNIST | mean constraint satisfaction error: 0.0931 -> 0.0733 | Helped |
| Add CONGAN constraint conditioning and training to WGAN | WGAN · CelebA | image quality by discriminator score: 20.31 (WGAN generator) versus 18.32–20.00 (CONGAN generator) | No clear effect |
| Add CONGAN constraint conditioning and training to WGAN | WGAN · Zappos50K | image quality by discriminator score: 60.31 -> 5.43 | Hurt |
| Increase the input set from 1 to 10 constraints (ablation) | CONGAN · CelebA | mean constraint satisfaction error: 0.0885 -> 0.1325 | Hurt |
| Increase the input set from 1 to 10 constraints (ablation) | CONGAN · Zappos50K | mean constraint satisfaction error: 0.0950 -> 0.1066 | Hurt |
| Balance constraint sampling over color bins instead of sampling only uniformly over images | CONGAN · Zappos50K | constraint satisfaction | Helped |
Closest papers by meaning
- DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data (CVPR 2017 · similarity 0.90)
- On Leveraging Pretrained GANs for Generation with Limited Data (ICML 2020 · similarity 0.90)
- Cluster-Guided Image Synthesis With Unconditional Models (CVPR 2022 · similarity 0.90)
- Are GANs Created Equal? A Large-Scale Study (NeurIPS 2018 · similarity 0.90)
- LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation (ICLR 2017 · similarity 0.89)
- Instance-Conditioned GAN (NeurIPS 2021 · similarity 0.89)
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