পদ্ধতির তুলনা করুন

	সূক্ষ্ম-সমন্বিত জেনারেটিভ অ্যাডভারসারিয়াল নেটওয়ার্ক	জেনারেটিভ অ্যাডভারসারিয়াল নেটওয়ার্ক
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2014 (GAN); 2019–2020 (fine-tuning paradigm)	2014
প্রবর্তক	Goodfellow, I. et al. (GAN); fine-tuning practice established ~2019–2020	Goodfellow, I. et al.
ধরন	Generative model (adversarial training + transfer)	Generative deep learning (adversarial two-network game)
মৌলিক উৎস	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.	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.
অপর নাম	Fine-Tuned GAN, GAN Fine-Tuning, Domain-Adapted GAN, Transfer GAN	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
সম্পর্কিত	6	4
সারসংক্ষেপ	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.	A Generative Adversarial Network (GAN), introduced by Ian Goodfellow and colleagues in 2014, produces realistic synthetic data through the competition of two neural networks — a generator and a discriminator. It is widely used for image synthesis, data augmentation, and distribution estimation.