Research map / Federated learning and privacy / Personalized and heterogeneous FL
Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a pretrained StyleGAN2 visual prior instead of learning the generator from scratch (ablation) | VPGAN · VIS-TH | Rank-1: 52.85 -> 76.19 | Helped |
| Add MSCA to the encoder (ablation) | VPGAN · VIS-TH | Rank-1: 71.90 -> 76.19 | Helped |
| Use federated training with MPR rather than FedProx | VPFL · Global Test Avg. | Rank-1: 61.98 -> 69.80 | Helped |
| Add Model Proximity Regularization (ablation) | VPFL · Global Test Avg. | Rank-1: 66.49 -> 69.80 | Helped |
| Use federated training rather than training one local-only model per institution | VPFL · Global Test Avg. | Rank-1: Local Only C1: 38.41; VPFL: 69.80 | Helped |
| Fuse the outputs of independently trained local models | Fused strategy · Global Test Avg. | Rank-1: 37.39 | Hurt |
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