방법 비교

	Semantic segmentation	컨볼루션 신경망을 이용한 전이 학습
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2015	2010–2014
창시자	Long, J., Shelhamer, E., & Darrell, T.	Pan, S. J. & Yang, Q. (transfer learning framework); popularized for CNNs by Yosinski et al. and Razavian et al.
유형	Dense prediction / pixel-wise classification	Transfer learning applied to convolutional neural networks
원전	Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
별칭	pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation	TL-CNN, pretrained CNN, CNN fine-tuning, feature-extracting CNN
관련	5	4
요약	Semantic segmentation assigns a class label to every pixel in an image, producing a dense, category-annotated map of the scene. Unlike object detection, which draws bounding boxes, it delineates the exact spatial extent of each class, making it indispensable in medical imaging, autonomous driving, satellite analysis, and any task where precise region boundaries matter.	Transfer Learning with CNN reuses a convolutional neural network that has already been trained on a large dataset — most commonly ImageNet — and adapts its learned feature detectors to a new, often smaller target dataset. This lets researchers achieve strong image-recognition performance without the massive compute and data resources required to train a CNN from scratch.