Linganisha mbinu

	Ugawaji wa Vielelezo Unaosimamiwa Kijitegemea	Jifunze kwa Kujisimamia
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2021–2022	2018–2020
Mwanzilishi	Wang et al. (FreeSOLO); Caron et al. (DINO)	LeCun, Y. and community (formalized ~2018–2020)
Aina	Self-supervised deep learning for pixel-level object delineation	Representation learning paradigm
Chanzo asilia	Wang, X., Zhu, Z., Cao, G., Yao, Z., Jiang, Z., & Ye, J. (2022). FreeSOLO: Learning to Segment Objects without Annotations. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 14176–14186.	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	SSIS, unsupervised instance segmentation, label-free instance segmentation, self-supervised mask prediction	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Zinazohusiana	4	3
Muhtasari	Self-supervised instance segmentation learns to detect and delineate individual object instances in images without any human-annotated masks or bounding boxes. Instead of relying on costly pixel-level labels, it exploits self-supervised pretraining, multi-view consistency, and pseudo-label generation to discover and segment objects purely from raw image data.	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.