Bandingkan kaedah

	Pembelajaran Pindahan dengan Rangkaian Neural Konvolusi	Klasifikasi Imej
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning
Tahun asal	2010–2014	2012 (deep CNN era); conceptual roots 1989 (LeCun)
Pengasas	Pan, S. J. & Yang, Q. (transfer learning framework); popularized for CNNs by Yosinski et al. and Razavian et al.	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.
Jenis	Transfer learning applied to convolutional neural networks	Supervised classification task
Sumber perintis	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.	Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NeurIPS), 25, 1097–1105.
Alias	TL-CNN, pretrained CNN, CNN fine-tuning, feature-extracting CNN	visual classification, image recognition, CNN-based classification, visual categorization
Berkaitan	4	5
Ringkasan	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.	Image classification is the task of assigning a single semantic label to an entire image from a fixed set of categories. Modern approaches rely on deep convolutional neural networks (CNNs) or Vision Transformers (ViTs) trained end-to-end on large labeled datasets such as ImageNet, achieving superhuman accuracy on many benchmarks and underpinning applications from medical imaging to autonomous vehicles.