So sánh phương pháp

	Phân đoạn ngữ nghĩa yếu giám sát	Học tăng cường tự giám sát
Lĩnh vực	Học sâu	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2014–2016	2018–2020
Người khởi xướng	Multiple contributors; Class Activation Mapping (Zhou et al., 2016) is foundational	LeCun, Y. and community (formalized ~2018–2020)
Loại	Pixel-level classification with image-level or coarse supervision	Representation learning paradigm
Công trình gốc	Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning Deep Features for Discriminative Localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2921–2929.	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/
Tên gọi khác	WSSS, weak-label segmentation, image-level supervised segmentation, weakly-labeled pixel classification	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Liên quan	4	3
Tóm tắt	Weakly Supervised Semantic Segmentation (WSSS) trains pixel-level scene parsers using only cheap, coarse annotations — typically image-level class tags — instead of costly dense pixel masks. By generating proxy pseudo-labels from a classification network (via Class Activation Maps or similar localisation cues) and iteratively refining them, WSSS brings full-supervision accuracy within reach at a fraction of the annotation cost.	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.