Linganisha mbinu

	Uainishaji wa Picha kwa Njia ya Nusu-Simamizi	Uainishaji wa Picha
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2013–2020	2012 (deep CNN era); conceptual roots 1989 (LeCun)
Mwanzilishi	Lee, D.-H. (pseudo-label); Sohn et al. (FixMatch)	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.
Aina	Semi-supervised deep learning	Supervised classification task
Chanzo asilia	Lee, D.-H. (2013). Pseudo-Label: The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks. ICML 2013 Workshop on Challenges in Representation Learning.	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	SSL image classification, semi-supervised CNN classification, pseudo-label image classification, label-efficient image classification	visual classification, image recognition, CNN-based classification, visual categorization
Zinazohusiana	5	5
Muhtasari	Semi-supervised image classification trains deep neural networks on a small set of labeled images together with a much larger pool of unlabeled images. Techniques such as pseudo-labeling, consistency regularization, and confidence thresholding allow the model to leverage the structure of unlabeled data, dramatically reducing the need for expensive manual annotation while approaching fully-supervised accuracy.	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.