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| 도메인 적응형 합성곱 신경망× | 도메인 적응 순환 신경망× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2015–2017 | 2010s |
| 창시자≠ | Ganin, Y. & Lempitsky, V. (domain-adversarial framework); Tzeng et al. (ADDA) | Ganin et al.; Pan & Yang (domain adaptation frameworks applied to RNNs) |
| 유형≠ | Domain-adaptive deep learning model | Domain-adaptive sequential model |
| 원전≠ | 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 ↗ | Ganin, Y., Ustunova, 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-CNN, domain adaptation CNN, domain-adaptive deep convolutional network, CNN with domain adaptation | DA-RNN, domain-adaptive RNN, domain-adapted recurrent network, cross-domain RNN |
| 관련≠ | 5 | 6 |
| 요약≠ | 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. | A Domain-adaptive Recurrent Neural Network (DA-RNN) is a recurrent neural network trained on a source domain and adapted to a target domain using domain adaptation techniques such as adversarial training, feature alignment, or fine-tuning. It enables sequential models to generalise across domains when labeled target-domain data is scarce or unavailable. |
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