Machine learningDeep learning / NLP / CV
Fine-Tuned Convolutional Neural Network
Fine-tuning a CNN means starting from a network already trained on a large dataset — typically ImageNet — and continuing training on a smaller target dataset so the model adapts its learned visual features to a new task. This approach dramatically reduces the data and compute required to reach strong performance compared with training from scratch.
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Sources
- Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems, 27. link ↗
- Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016). Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Transactions on Medical Imaging, 35(5), 1299–1312. DOI: 10.1109/TMI.2016.2535302 ↗
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Domain-adaptive Convolutional Neural NetworkFine-Tuned Generative Adversarial NetworkFine-Tuned Image ClassificationFine-Tuned Multilayer PerceptronFine-Tuned Semantic SegmentationFine-Tuned Variational AutoencoderFine-Tuned Vision TransformerSelf-supervised convolutional neural networkSemi-supervised Convolutional Neural NetworkTransfer Learning with Convolutional Neural NetworkTransfer Learning with Image ClassificationTransfer Learning with Object DetectionWeakly supervised convolutional neural network