Transfer Learning with Convolutional Neural Network
Transfer Learning with CNN reuses a convolutional neural network that has already been trained on a large dataset — most commonly ImageNet — and adapts its learned feature detectors to a new, often smaller target dataset. This lets researchers achieve strong image-recognition performance without the massive compute and data resources required to train a CNN from scratch.
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Method map
The neighbourhood of related methods — select a node to explore.
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Источники
- Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI: 10.1109/TKDE.2009.191 ↗
- Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems (NeurIPS), 27, 3320–3328. link ↗
Как цитировать эту страницу
ScholarGate. (2026, June 3). Transfer Learning with Convolutional Neural Network (Feature Extraction and Fine-Tuning). ScholarGate. https://scholargate.app/ru/deep-learning/transfer-learning-with-convolutional-neural-network
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Дообученная (fine-tuned) свёрточная нейронная сетьГлубокое обучение↔ compare
- Классификация изображенийГлубокое обучение↔ compare
- Обнаружение объектовГлубокое обучение↔ compare
- Семантическая сегментацияГлубокое обучение↔ compare
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