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| 도메인 적응형 다층 퍼셉트론× | 도메인 적응형 합성곱 신경망× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2006–2016 | 2015–2017 |
| 창시자≠ | Ben-David et al.; Ganin et al. | Ganin, Y. & Lempitsky, V. (domain-adversarial framework); Tzeng et al. (ADDA) |
| 유형≠ | Domain adaptation of feedforward neural network | Domain-adaptive deep learning model |
| 원전≠ | Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., & Vaughan, J. W. (2010). A theory of learning from different domains. Machine Learning, 79(1–2), 151–175. DOI ↗ | 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-MLP, domain-adaptive MLP, domain-adapted feedforward network, domain adaptation with MLP | DA-CNN, domain adaptation CNN, domain-adaptive deep convolutional network, CNN with domain adaptation |
| 관련 | 5 | 5 |
| 요약≠ | A domain-adaptive multilayer perceptron (DA-MLP) is a feedforward neural network trained to learn representations that are useful across a labeled source domain and an unlabeled or differently distributed target domain. By minimizing both a task loss and a domain-discrepancy objective, the MLP generalizes to the target domain with little or no target-domain labels. | 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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