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GAN Compression: Efficient Architectures for Interactive Conditional GANs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Compress CycleGAN with GAN Compression | CycleGAN · horse→zebra | FID: 61.53 -> 71.81 | Hurt |
| Use GAN Compression instead of Shu et al.'s method | CycleGAN · horse→zebra | FID: 96.15 -> 71.81 | Helped |
| Compress MUNIT with GAN Compression | MUNIT · edges→shoes | FID: 30.13 -> 31.48 | Hurt |
| Compress Pix2pix with GAN Compression | Pix2pix · edges→shoes | FID: 24.18 -> 26.60 | Hurt |
| Compress Pix2pix with GAN Compression | Pix2pix · Cityscapes | mIoU: 42.06 -> 40.77 | Hurt |
| Compress GauGAN with GAN Compression | GauGAN · Cityscapes | mIoU: 62.18 -> 61.22 | Hurt |
| Compress GauGAN with Fast GAN Compression | GauGAN · COCO-Stuff | mIoU: 38.38 -> 35.34 | Hurt |
| Compress GauGAN with Fast GAN Compression | GauGAN · COCO-Stuff | FID: 21.96 -> 25.06 | Hurt |
| Use intermediate feature distillation and the full compression method instead of from-scratch training (ablation) | Pix2pix · Cityscapes | mIoU: 32.57 -> 40.77 | Helped |
| Use intermediate distillation rather than teacher-output distillation (ablation) | Pix2pix · Cityscapes | mIoU: 35.67 -> 38.26 | Helped |
| Decompose ResBlock convolutions rather than downsample convolutions at ngf=64 (ablation) | CycleGAN generator · horse→zebra | FID: 68.72 -> 62.95 | Helped |
| Decompose ResBlock convolutions rather than upsample convolutions at ngf=16 (ablation) | CycleGAN generator · horse→zebra | FID: 95.54 -> 79.49 | Helped |
Closest papers by meaning
- Content-Aware GAN Compression (CVPR 2021 · similarity 0.93)
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- Compressing Image-to-Image Translation GANs Using Local Density Structures on Their Learned Manifold (AAAI 2024 · similarity 0.91)
- AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks (ICML 2020 · similarity 0.91)
- Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis (ICLR 2021 · similarity 0.91)
- The GAN is dead; long live the GAN! A Modern GAN Baseline (NeurIPS 2024 · similarity 0.89)
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