Linganisha mbinu

	Uhamishaji wa Mafunzo kwa Mitandao ya Neura ya Kimkunjo	Uainishaji wa Picha
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2010–2014	2012 (deep CNN era); conceptual roots 1989 (LeCun)
Mwanzilishi	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.
Aina	Transfer learning applied to convolutional neural networks	Supervised classification task
Chanzo asilia	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.
Majina mbadala	TL-CNN, pretrained CNN, CNN fine-tuning, feature-extracting CNN	visual classification, image recognition, CNN-based classification, visual categorization
Zinazohusiana	4	5
Muhtasari	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.