Research map / Label-efficient and robust learning / Zero-/few-shot and meta-learning
Domain Disentangled Generative Adversarial Network for Zero-Shot Sketch-Based 3D Shape Retrieval
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add semantic alignment to the triplet-loss baseline (ablation) | DD-GAN · SHREC'13 | mAP: 0.231 -> 0.254 | Helped |
| Add domain disentangling and domain combination (ablation) | Baseline + SA · SHREC'13 | mAP: 0.254 -> 0.523 | Helped |
| Use unlabeled unseen samples in the transductive setting (ablation) | DD-GAN · SHREC'13 | mAP: 0.523 -> 0.551 | Helped |
| Use DD-GAN instead of CGN | zero-shot sketch-based 3D retrieval · SHREC'13 | mAP: 0.515 -> 0.551 | Helped |
| Use DD-GAN instead of CGN | zero-shot sketch-based 3D retrieval · SHREC'14 | mAP: 0.332 -> 0.371 | Helped |
Closest papers by meaning
- Deep Correlated Metric Learning for Sketch-based 3D Shape Retrieval (AAAI 2017 · similarity 0.88)
- Learning Cross-Domain Neural Networks for Sketch-Based 3D Shape Retrieval (AAAI 2016 · similarity 0.88)
- SketchGAN: Joint Sketch Completion and Recognition With Generative Adversarial Network (CVPR 2019 · similarity 0.87)
- Learning Barycentric Representations of 3D Shapes for Sketch-Based 3D Shape Retrieval (CVPR 2017 · similarity 0.87)
- Doodle to Object: Practical Zero-Shot Sketch-Based 3D Shape Retrieval (AAAI 2023 · similarity 0.87)
- Deep Sketch-Shape Hashing With Segmented 3D Stochastic Viewing (CVPR 2019 · similarity 0.87)
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