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Transfer Learning with Image Classification

Transfer Learning with Image Classification reuses a deep neural network backbone — typically a CNN or Vision Transformer — pretrained on a large dataset such as ImageNet, and adapts it to classify images in a new target domain. By inheriting general visual features from the source task, the approach achieves high accuracy with far fewer labeled images than training 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. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25. link ↗

Related methods

Fine-Tuned Convolutional Neural NetworkFine-Tuned Vision TransformerImage ClassificationTransfer Learning with Object Detection

Referenced by

Domain-adaptive image classificationFine-Tuned Image ClassificationImage ClassificationMultilingual Image ClassificationSemi-supervised Image ClassificationTransfer Learning with Instance SegmentationTransfer Learning with Object DetectionWeakly Supervised Image Classification

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ScholarGate — Transfer Learning with Image Classification (Transfer Learning with Pretrained Deep Neural Networks for Image Classification). Retrieved 2026-06-04 from https://scholargate.app/tr/deep-learning/transfer-learning-with-image-classification