Machine learningDeep learning / NLP / CV

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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Источники

  1. 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
  2. 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

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ScholarGateTransfer Learning with Convolutional Neural Network (Transfer Learning with Convolutional Neural Network (Feature Extraction and Fine-Tuning)). Получено 2026-06-15 из https://scholargate.app/ru/deep-learning/transfer-learning-with-convolutional-neural-network · Набор данных: https://doi.org/10.5281/zenodo.20539026