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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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Sources

  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 ↗

Related methods

Fine-Tuned Convolutional Neural NetworkImage ClassificationObject DetectionSemantic Segmentation

Referenced by

Domain-adaptive Convolutional Neural NetworkFine-Tuned Convolutional Neural NetworkMultilingual Convolutional Neural NetworkMultimodal Convolutional Neural NetworkSelf-supervised convolutional neural networkSemantic SegmentationSemi-supervised Convolutional Neural NetworkTransfer learning GANTransfer learning variational autoencoderTransfer Learning with Diffusion ModelTransfer Learning with Graph Neural NetworkTransfer Learning with Reinforcement Learning

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ScholarGate — Transfer Learning with Convolutional Neural Network (Transfer Learning with Convolutional Neural Network (Feature Extraction and Fine-Tuning)). Retrieved 2026-06-04 from https://scholargate.app/tr/deep-learning/transfer-learning-with-convolutional-neural-network