Research map / Label-efficient and robust learning / Domain adaptation and generalization
Constrained Deep Transfer Feature Learning and Its Applications
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add semi-supervised learning and the target-domain constraint to transfer feature learning | Two-layer DBM for thermal eye detection · VTFB | eye detection rate: 87.45% -> 93.54%; increase by 6.09% | Helped |
| Add the target-domain constraint to pairwise-data transfer (ablation) | Two-layer DBM for thermal eye detection · VTFB | eye detection rate: 89.48% -> 91.88% | Helped |
| Add semi-supervised learning with unpaired data to pairwise-data transfer (ablation) | Two-layer DBM for thermal eye detection · VTFB | eye detection rate: 89.48% -> 90.04% | Helped |
| Add both semi-supervised learning and the constraint (ablation) | RBM joint-probability transfer model · Layer-one transfer, hold-out pairwise data | reconstruction error: 0.0627 -> 0.0580 | Helped |
| Add both semi-supervised learning and the constraint (ablation) | RBM joint-probability transfer model · Layer-one transfer, hold-out pairwise data | log likelihood: -514.1 -> -476.9 | Helped |
| Add a constraint to pairwise-data transfer (ablation) | RBM joint-probability transfer model · Layer-one transfer, hold-out pairwise data | reconstruction error: 0.0627 -> 0.0588 | Helped |
| Add semi-supervised learning to pairwise-data transfer (ablation) | RBM joint-probability transfer model · Layer-one transfer, hold-out pairwise data | log likelihood: -514.1 -> -484.8 | Helped |
| Transfer in both feature-learning layers rather than one layer (ablation) | Constrained deep transfer feature learning · VTFB eye detection | eye detection rate | Helped |
| Transfer in the deeper feature layer rather than the shallow feature layer (ablation) | Constrained deep transfer feature learning · VTFB eye detection | eye detection rate | Helped |
| Use constrained deep transfer features instead of DBM features without transfer | Thermal eye detector · MAHNOB Laughter | detection rate: 83.3% -> 87.72% | Helped |
| Use constrained deep transfer features instead of the reported baseline detector | Thermal eye detector · NVIE | detection rate: 68% -> 81.60% | Helped |
| Use constrained deep transfer features instead of conventional DBM features | Cross-view facial expression recognition · Multi-Pie | classification accuracy | Helped |
Closest papers by meaning
- Features are fate: a theory of transfer learning in high-dimensional regression (ICML 2025 · similarity 0.84)
- What is being transferred in transfer learning? (NeurIPS 2020 · similarity 0.84)
- Feature Transfer Learning for Face Recognition With Under-Represented Data (CVPR 2019 · similarity 0.83)
- Pay Attention to Features, Transfer Learn Faster CNNs (ICLR 2020 · similarity 0.83)
- Transferring Knowledge From Large Foundation Models to Small Downstream Models (ICML 2024 · similarity 0.83)
- Closed-Form Linear-Probe Dataset Distillation for Pre-trained Vision Models (NeurIPS 2026 · similarity 0.82)
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