Research map / Image generation / GANs
Learning What and Where to Draw
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add bird location constraints through real keypoints | GAWWN · CUB | image quality and resolution: 64 × 64 -> 128 × 128 | Helped |
| Generate keypoints from text rather than requiring ground-truth keypoints | GAWWN · CUB | image quality | No clear effect |
| Remove keypoint location conditioning by zeroing keypoints during training (ablation) | GAWWN · CUB | image plausibility | Hurt |
| Condition image generation on human pose keypoints and text | GAWWN · MPII Human Pose | qualitative image quality | Mixed |
Closest papers by meaning
- Generating Multiple Objects at Spatially Distinct Locations (ICLR 2019 · similarity 0.88)
- DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data (CVPR 2017 · similarity 0.87)
- SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis (CVPR 2018 · similarity 0.87)
- Generative Adversarial Text to Image Synthesis (ICML 2016 · similarity 0.86)
- Large Scale GAN Training for High Fidelity Natural Image Synthesis (ICLR 2019 · similarity 0.86)
- PandA: Unsupervised Learning of Parts and Appearances in the Feature Maps of GANs (ICLR 2023 · similarity 0.86)
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