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| 약한 지도 학습 컨볼루션 신경망× | 이미지 분류× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2015–2016 | 2012 (deep CNN era); conceptual roots 1989 (LeCun) |
| 창시자≠ | Oquab, M. et al.; Zhou, B. et al. | Krizhevsky, A.; Sutskever, I.; Hinton, G. E. |
| 유형≠ | Weakly supervised deep learning | Supervised classification task |
| 원전≠ | 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 ↗ | Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NeurIPS), 25, 1097–1105. link ↗ |
| 별칭 | WS-CNN, weakly supervised CNN, CNN with weak labels, CNN with noisy labels | visual classification, image recognition, CNN-based classification, visual categorization |
| 관련 | 5 | 5 |
| 요약≠ | 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. | Image classification is the task of assigning a single semantic label to an entire image from a fixed set of categories. Modern approaches rely on deep convolutional neural networks (CNNs) or Vision Transformers (ViTs) trained end-to-end on large labeled datasets such as ImageNet, achieving superhuman accuracy on many benchmarks and underpinning applications from medical imaging to autonomous vehicles. |
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