Sammenlign metoder

	Selvovervåget konvolutionelt neuralt netværk	Transferlæring med konvolutionelle neurale netværk
Fagområde	Dyb læring	Dyb læring
Familie	Machine learning	Machine learning
Oprindelsesår	2018–2020	2010–2014
Ophavsperson	LeCun, Y. (CNN backbone); Chen et al. and He et al. (self-supervised visual frameworks)	Pan, S. J. & Yang, Q. (transfer learning framework); popularized for CNNs by Yosinski et al. and Razavian et al.
Type	Self-supervised deep learning	Transfer learning applied to convolutional neural networks
Oprindelig kilde	Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning (ICML 2020), PMLR 119, 1597–1607.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Aliasser	Self-supervised CNN, SSL-CNN, contrastive CNN, pretext-task CNN	TL-CNN, pretrained CNN, CNN fine-tuning, feature-extracting CNN
Relaterede	5	4
Resumé	A self-supervised convolutional neural network (CNN) learns powerful visual representations from unlabeled images by solving pretext tasks — such as contrastive instance discrimination or masked-patch prediction — and then fine-tunes on a small labeled set. This approach dramatically reduces dependence on large annotated datasets while preserving the spatial feature-extraction strengths of convolutional architectures.	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.