Metodebevisregistrering
Transfer learning GAN
Transfer Learning GAN initialises a Generative Adversarial Network — or both its generator and discriminator — from weights pretrained on a large source dataset, then fine-tunes the network on a smaller target dataset. This approach allows high-quality generative modelling even when target-domain data are scarce, by reusing low- and mid-level feature representations learned at scale.
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Transfer Learning with Generative Adversarial Networks
Taksonomisk metoderegistrering · ml-model / deep-learning
- Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems (NeurIPS), 27, 2672–2680. · URL
- Wang, Y. & Ramanan, D. (2018). Transferring GANs: generating images from limited data. European Conference on Computer Vision (ECCV), 11205, 220–236. · DOI 10.1007/978-3-030-01231-1_14
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