השוואת שיטות
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| רשת קונבולוציה מפוקחת באופן חלש× | רשת קונבולוציה למידה עצמית-מונחית× | |
|---|---|---|
| תחום | למידה עמוקה | למידה עמוקה |
| משפחה | Machine learning | Machine learning |
| שנת המקור≠ | 2015–2016 | 2018–2020 |
| הוגה השיטה≠ | Oquab, M. et al.; Zhou, B. et al. | LeCun, Y. (CNN backbone); Chen et al. and He et al. (self-supervised visual frameworks) |
| סוג≠ | Weakly supervised deep learning | Self-supervised deep learning |
| מקור מכונן≠ | Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2921–2929. DOI ↗ | Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning (ICML 2020), PMLR 119, 1597–1607. link ↗ |
| כינויים | WS-CNN, weakly supervised CNN, CNN with weak labels, CNN with noisy labels | Self-supervised CNN, SSL-CNN, contrastive CNN, pretext-task CNN |
| קשורות | 5 | 5 |
| תקציר≠ | A weakly supervised CNN is a convolutional neural network trained with incomplete, coarse, or noisy annotations instead of full pixel-level or bounding-box labels. Typical weak labels include image-level class tags, partial annotations, or crowd-sourced noisy labels. The model learns to classify and often to roughly localize objects using these cheaper, lower-quality supervision signals. | A self-supervised convolutional neural network (CNN) learns powerful visual representations from unlabeled images by solving pretext tasks — such as contrastive instance discrimination or masked-patch prediction — and then fine-tunes on a small labeled set. This approach dramatically reduces dependence on large annotated datasets while preserving the spatial feature-extraction strengths of convolutional architectures. |
| ScholarGateמערך נתונים ↗ |
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