Comparar métodos

	Aprendizaje por transferencia con Red Neuronal Convolucional	Detección de objetos
Campo	Aprendizaje profundo	Aprendizaje profundo
Familia	Machine learning	Machine learning
Año de origen	2010–2014	2014–2016
Autor original	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)
Tipo	Transfer learning applied to convolutional neural networks	Supervised deep learning (region proposal or single-shot)
Fuente seminal	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.	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.
Alias	TL-CNN, pretrained CNN, CNN fine-tuning, feature-extracting CNN	visual object detection, image object localization, region-based object detection, bounding-box detection
Relacionados	4	3
Resumen	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.