Linganisha mbinu

	Kigeuzi cha Taswira Kinachosimamiwa Kidogo	Jifunze kwa Kujisimamia
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2021–2022	2018–2020
Mwanzilishi	Dosovitskiy et al. (ViT); weak supervision paradigm from Zhou and others	LeCun, Y. and community (formalized ~2018–2020)
Aina	Self-attention image model with weakly supervised training	Representation learning paradigm
Chanzo asilia	Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations (ICLR).	LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/
Majina mbadala	WS-ViT, weakly supervised ViT, weak supervision with vision transformer, ViT with weak labels	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Zinazohusiana	4	3
Muhtasari	Weakly Supervised Vision Transformer (WS-ViT) trains a Vision Transformer on image data that lacks precise pixel-level annotations, instead using cheaper, noisier supervision such as image-level class tags, bounding boxes, or web-scraped text. The global self-attention mechanism of the transformer makes it especially capable of localising objects and learning discriminative features from these incomplete labels.	Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples.