Сравнение методов

	Дообученная генеративно-состязательная сеть	Дообученный Vision Transformer
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2014 (GAN); 2019–2020 (fine-tuning paradigm)	2020-2021
Автор метода	Goodfellow, I. et al. (GAN); fine-tuning practice established ~2019–2020	Dosovitskiy, A. et al. (Google Brain)
Тип	Generative model (adversarial training + transfer)	Transfer learning / fine-tuning of attention-based image model
Основополагающий источник	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.	Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations (ICLR 2021).
Другие названия	Fine-Tuned GAN, GAN Fine-Tuning, Domain-Adapted GAN, Transfer GAN	Fine-Tuned ViT, ViT fine-tuning, Vision Transformer transfer learning, ViT downstream adaptation
Связанные	6	5
Сводка	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.	Fine-Tuned Vision Transformer adapts a large pre-trained ViT model — which splits images into fixed-size patches and processes them through self-attention layers — to a new image classification or recognition task using a relatively small labeled dataset. It achieves state-of-the-art accuracy in computer vision by leveraging rich representations learned during large-scale pre-training.