Methoden vergleichen

	Schwache CNNs (Weakly Supervised Convolutional Neural Network)	Feinabgestimmtes Convolutional Neural Network
Fachgebiet	Deep Learning	Deep Learning
Familie	Machine learning	Machine learning
Entstehungsjahr	2015–2016	2012–2014
Urheber	Oquab, M. et al.; Zhou, B. et al.	Yosinski, J. et al. (theoretical basis); practice widespread from Krizhevsky et al. 2012 onward
Typ	Weakly supervised deep learning	Transfer learning technique (supervised fine-tuning)
Wegweisende Quelle	Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2921–2929.	Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems, 27.
Aliasnamen	WS-CNN, weakly supervised CNN, CNN with weak labels, CNN with noisy labels	Fine-tuned CNN, CNN fine-tuning, CNN transfer learning with fine-tuning, adapted convolutional network
Verwandt	5	5
Zusammenfassung	A weakly supervised CNN is a convolutional neural network trained with incomplete, coarse, or noisy annotations instead of full pixel-level or bounding-box labels. Typical weak labels include image-level class tags, partial annotations, or crowd-sourced noisy labels. The model learns to classify and often to roughly localize objects using these cheaper, lower-quality supervision signals.	Fine-tuning a CNN means starting from a network already trained on a large dataset — typically ImageNet — and continuing training on a smaller target dataset so the model adapts its learned visual features to a new task. This approach dramatically reduces the data and compute required to reach strong performance compared with training from scratch.