পদ্ধতির তুলনা করুন

	ফাইন-টিউনড কনভোল্যুশনাল নিউরাল নেটওয়ার্ক	ফাইন-টিউনড ভিশন ট্রান্সফরমার
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2012–2014	2020-2021
প্রবর্তক	Yosinski, J. et al. (theoretical basis); practice widespread from Krizhevsky et al. 2012 onward	Dosovitskiy, A. et al. (Google Brain)
ধরন	Transfer learning technique (supervised fine-tuning)	Transfer learning / fine-tuning of attention-based image model
মৌলিক উৎস	Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems, 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).
অপর নাম	Fine-tuned CNN, CNN fine-tuning, CNN transfer learning with fine-tuning, adapted convolutional network	Fine-Tuned ViT, ViT fine-tuning, Vision Transformer transfer learning, ViT downstream adaptation
সম্পর্কিত	5	5
সারসংক্ষেপ	Fine-tuning a CNN means starting from a network already trained on a large dataset — typically ImageNet — and continuing training on a smaller target dataset so the model adapts its learned visual features to a new task. This approach dramatically reduces the data and compute required to reach strong performance compared with training from scratch.	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.