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| 컨볼루션 신경망을 이용한 전이 학습× | 객체 탐지× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2010–2014 | 2014–2016 |
| 창시자≠ | Pan, S. J. & Yang, Q. (transfer learning framework); popularized for CNNs by Yosinski et al. and Razavian et al. | Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO) |
| 유형≠ | Transfer learning applied to convolutional neural networks | Supervised deep learning (region proposal or single-shot) |
| 원전≠ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ | Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 580–587. DOI ↗ |
| 별칭 | TL-CNN, pretrained CNN, CNN fine-tuning, feature-extracting CNN | visual object detection, image object localization, region-based object detection, bounding-box detection |
| 관련≠ | 4 | 3 |
| 요약≠ | 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. | Object detection is a computer vision task in which a deep neural network simultaneously locates and classifies every instance of one or more object categories within an image, producing a bounding box and a class label for each detected object. Modern detectors — from the R-CNN family to YOLO and DETR — achieve near-human accuracy at real-time speeds on standard benchmarks. |
| ScholarGate데이터셋 ↗ |
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