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Machine learningDeep learning / NLP / CV

Fine-Tuned Generative Adversarial Network

A Fine-Tuned GAN starts from a large pre-trained generative adversarial network and continues adversarial training on a smaller target dataset, allowing the model to synthesize high-quality samples in a new domain without training from scratch. This transfer approach dramatically reduces data and compute requirements while preserving the rich feature representations learned during pre-training.

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Sources

  1. 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. link ↗
  2. Mo, S., Cho, M., & Shin, J. (2020). Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs. CVPR 2020 Workshop on AI for Content Creation. link ↗

Related methods

Fine-Tuned Convolutional Neural NetworkFine-Tuned Diffusion ModelFine-Tuned Variational AutoencoderFine-Tuned Vision TransformerGenerative Adversarial NetworkTransfer learning GAN

Referenced by

Domain-adaptive GANFine-Tuned Diffusion ModelFine-Tuned Variational AutoencoderTransfer learning GANTransfer learning variational autoencoder

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ScholarGate — Fine-Tuned Generative Adversarial Network (Fine-Tuned Generative Adversarial Network (Domain-Adaptive GAN via Transfer)). Retrieved 2026-06-04 from https://scholargate.app/tr/deep-learning/fine-tuned-generative-adversarial-network