Comparer des méthodes

	Apprentissage par transfert avec réseau neuronal convolutif	Détection d'objets
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2010–2014	2014–2016
Auteur d'origine	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)
Type	Transfer learning applied to convolutional neural networks	Supervised deep learning (region proposal or single-shot)
Source fondatrice	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
Apparentées	4	3
Résumé	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.