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| Overførselslæring med Variational Autoencoder× | Transferlæring med konvolutionelle neurale netværk× | |
|---|---|---|
| Fagområde | Dyb læring | Dyb læring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2014 (VAE); 2010 (transfer learning survey) | 2010–2014 |
| Ophavsperson≠ | Kingma, D. P. & Welling, M. (VAE); transfer learning framework from Pan & Yang | Pan, S. J. & Yang, Q. (transfer learning framework); popularized for CNNs by Yosinski et al. and Razavian et al. |
| Type≠ | Generative model with transferred encoder/decoder | Transfer learning applied to convolutional neural networks |
| Oprindelig kilde≠ | Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR 2014). link ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| Aliasser | TL-VAE, pretrained VAE, VAE transfer learning, fine-tuned variational autoencoder | TL-CNN, pretrained CNN, CNN fine-tuning, feature-extracting CNN |
| Relaterede≠ | 6 | 4 |
| Resumé≠ | Transfer Learning with a Variational Autoencoder (TL-VAE) reuses an encoder and/or decoder pre-trained on a large source dataset and adapts it to a smaller target domain. By inheriting a rich probabilistic latent space rather than starting from random weights, TL-VAE dramatically reduces the amount of target-domain data needed for high-quality generation or representation learning. | 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. |
| ScholarGateDatasæt ↗ |
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