Methoden vergleichen

	Transfer Learning mit Convolutional Neural Network	Objekterkennung
Fachgebiet	Deep Learning	Deep Learning
Familie	Machine learning	Machine learning
Entstehungsjahr	2010–2014	2014–2016
Urheber	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)
Typ	Transfer learning applied to convolutional neural networks	Supervised deep learning (region proposal or single-shot)
Wegweisende Quelle	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.
Aliasnamen	TL-CNN, pretrained CNN, CNN fine-tuning, feature-extracting CNN	visual object detection, image object localization, region-based object detection, bounding-box detection
Verwandt	4	3
Zusammenfassung	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.