Research map / Image generation / GANs
Finding Tiny Faces in the Wild With Generative Adversarial Network
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add the super-resolution and refinement GAN to the baseline detector | MB-FCN · WIDER FACE validation Hard | AP: 0.858 -> 0.873 | Helped |
| add the refinement network (ablation) | Tiny Faces GAN · WIDER FACE validation Hard | AP: 0.863 -> 0.873 | Helped |
| add the refinement network (ablation) | Tiny Faces GAN · WIDER FACE validation Easy | AP: 0.940 -> 0.944 | Helped |
| add the refinement network (ablation) | Tiny Faces GAN · WIDER FACE validation Medium | AP: 0.929 -> 0.933 | Helped |
| add adversarial loss (ablation) | Tiny Faces GAN · WIDER FACE validation Easy | AP: 0.935 -> 0.944 | Helped |
| add adversarial loss (ablation) | Tiny Faces GAN · WIDER FACE validation Medium | AP: 0.925 -> 0.933 | Helped |
| add adversarial loss (ablation) | Tiny Faces GAN · WIDER FACE validation Hard | AP: 0.867 -> 0.873 | Helped |
| add classification loss (ablation) | Tiny Faces GAN · WIDER FACE validation Easy | AP: 0.936 -> 0.944 | Helped |
| add classification loss (ablation) | Tiny Faces GAN · WIDER FACE validation Medium | AP: 0.927 -> 0.933 | Helped |
| add classification loss (ablation) | Tiny Faces GAN · WIDER FACE validation Hard | AP: 0.865 -> 0.873 | Helped |
Closest papers by meaning
- GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR 2021 · similarity 0.88)
- Perceptual Generative Adversarial Networks for Small Object Detection (CVPR 2017 · similarity 0.88)
- Unsupervised Face Normalization With Extreme Pose and Expression in the Wild (CVPR 2019 · similarity 0.87)
- Feature Super-Resolution: Make Machine See More Clearly (CVPR 2018 · similarity 0.87)
- Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis (NeurIPS 2017 · similarity 0.87)
- FaceID-GAN: Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis (CVPR 2018 · similarity 0.86)
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