Porovnať metódy

	Konvolučná neuronová sieť so samoučiacim sa dohľadom	Samoučiací sa Transformer
Odbor	Hlboké učenie	Hlboké učenie
Rodina	Machine learning	Machine learning
Rok vzniku	2018–2020	2017–2019
Tvorca	LeCun, Y. (CNN backbone); Chen et al. and He et al. (self-supervised visual frameworks)	Vaswani et al. (architecture); Devlin et al. (BERT self-supervised paradigm)
Typ	Self-supervised deep learning	Self-supervised deep learning model
Pôvodný zdroj	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.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.
Ďalšie názvy	Self-supervised CNN, SSL-CNN, contrastive CNN, pretext-task CNN	SSL Transformer, self-supervised pretraining, masked self-attention pretraining, contrastive transformer
Príbuzné	5	5
Zhrnutie	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.	A self-supervised Transformer is a Transformer network pretrained using automatically constructed supervision signals — such as masked token prediction or next-sentence prediction — rather than human-annotated labels. The resulting representations are then fine-tuned or probed on downstream tasks. BERT, GPT, and ViT (Vision Transformer in masked-image modeling mode) are the most widely known instantiations of this paradigm.