Research map / Label-efficient and robust learning / Recognition and long-tail
Longitudinal Face Modeling via Temporal Deep Restricted Boltzmann Machines
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed progression rather than IAAP to synthesize faces | FG-NET age progression · FG-NET, set B versus set B' | MAE (years) from age estimation: 6.29 -> 5.96 | Helped |
| Replace mean-face references with RBM feature-transfer references (ablation) | Proposed age progression · Face aging sequences | preservation of age-specific facial details | Helped |
| Add synthesized wrinkles from age-group and region-specific RBMs (ablation) | TRBM texture progression · Later-age face synthesis | wrinkle detail | Helped |
Closest papers by meaning
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- Automatic Face Aging in Videos via Deep Reinforcement Learning (CVPR 2019 · similarity 0.89)
- Continuous Face Aging via Self-Estimated Residual Age Embedding (CVPR 2021 · similarity 0.89)
- From Cradle to Cane: A Two-Pass Framework for High-Fidelity Lifespan Face Aging (NeurIPS 2025 · similarity 0.87)
- A Call to Reflect on Evaluation Practices for Age Estimation: Comparative Analysis of the State-of-the-Art and a Unified Benchmark (CVPR 2024 · similarity 0.85)
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