Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Pooljuhendatud pildiklassifikatsioon× | Ülekandeõpe pildiklassifitseerimisel× | |
|---|---|---|
| Valdkond | Süvaõpe | Süvaõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2013–2020 | 2010–2012 |
| Looja≠ | Lee, D.-H. (pseudo-label); Sohn et al. (FixMatch) | Pan, S. J. & Yang, Q. (transfer learning framework); Krizhevsky, Sutskever & Hinton (deep CNN backbone) |
| Tüüp≠ | Semi-supervised deep learning | Transfer learning / supervised classification |
| Algallikas≠ | Lee, D.-H. (2013). Pseudo-Label: The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks. ICML 2013 Workshop on Challenges in Representation Learning. link ↗ | Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| Rööpnimetused | SSL image classification, semi-supervised CNN classification, pseudo-label image classification, label-efficient image classification | pretrained CNN image classification, fine-tuned image classifier, domain-adapted image classifier, TL-IC |
| Seotud≠ | 5 | 4 |
| Kokkuvõte≠ | Semi-supervised image classification trains deep neural networks on a small set of labeled images together with a much larger pool of unlabeled images. Techniques such as pseudo-labeling, consistency regularization, and confidence thresholding allow the model to leverage the structure of unlabeled data, dramatically reducing the need for expensive manual annotation while approaching fully-supervised accuracy. | Transfer Learning with Image Classification reuses a deep neural network backbone — typically a CNN or Vision Transformer — pretrained on a large dataset such as ImageNet, and adapts it to classify images in a new target domain. By inheriting general visual features from the source task, the approach achieves high accuracy with far fewer labeled images than training from scratch. |
| ScholarGateAndmestik ↗ |
|
|