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| 약한 지도 의미론적 분할× | 자기 지도 학습× | |
|---|---|---|
| 분야≠ | 딥러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2014–2016 | 2018–2020 |
| 창시자≠ | Multiple contributors; Class Activation Mapping (Zhou et al., 2016) is foundational | LeCun, Y. and community (formalized ~2018–2020) |
| 유형≠ | Pixel-level classification with image-level or coarse supervision | Representation learning paradigm |
| 원전≠ | 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. DOI ↗ | 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/ link ↗ |
| 별칭 | WSSS, weak-label segmentation, image-level supervised segmentation, weakly-labeled pixel classification | SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning |
| 관련≠ | 4 | 3 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
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