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| 도메인 적응형 비전 트랜스포머× | 도메인 적응형 합성곱 신경망× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2021–2023 | 2015–2017 |
| 창시자≠ | Multiple groups (Yang et al., 2023; Xu et al., 2021; Ma et al., 2022) | Ganin, Y. & Lempitsky, V. (domain-adversarial framework); Tzeng et al. (ADDA) |
| 유형≠ | Domain adaptation + Vision Transformer ensemble | Domain-adaptive deep learning model |
| 원전≠ | Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. International Conference on Learning Representations (ICLR). link ↗ | Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(59), 1–35. link ↗ |
| 별칭 | DA-ViT, Domain Adaptation with Vision Transformer, ViT with Domain Adaptation, Domain-Adaptive ViT | DA-CNN, domain adaptation CNN, domain-adaptive deep convolutional network, CNN with domain adaptation |
| 관련 | 5 | 5 |
| 요약≠ | Domain-Adaptive Vision Transformer (DA-ViT) applies domain adaptation techniques — such as adversarial alignment, self-training, or attention-level bridging — on top of a pretrained Vision Transformer backbone to transfer visual knowledge from a labeled source domain to an unlabeled or lightly labeled target domain, reducing the distribution shift that limits standard ViT fine-tuning. | A domain-adaptive CNN trains a convolutional network on a labeled source domain and adapts its learned feature representations to an unlabeled or lightly labeled target domain, bridging the distribution gap so that visual classifiers transfer reliably across datasets, sensors, or imaging conditions without full re-annotation. |
| ScholarGate데이터셋 ↗ |
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