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Transfer Learning with Variational Autoencoder

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.

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Sources

  1. Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR 2014). link ↗
  2. Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI: 10.1109/TKDE.2009.191 ↗

Related methods

Fine-Tuned Generative Adversarial NetworkFine-Tuned Variational AutoencoderGenerative Adversarial NetworkSemi-supervised Variational AutoencoderTransfer Learning with Convolutional Neural NetworkVariational Autoencoder

Referenced by

Fine-Tuned Variational AutoencoderMultilingual variational autoencoderSemi-supervised Variational Autoencoder

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ScholarGate — Transfer learning variational autoencoder (Transfer Learning with Variational Autoencoder). Retrieved 2026-06-04 from https://scholargate.app/tr/deep-learning/transfer-learning-variational-autoencoder