방법 비교

	EfficientNet	ResNet (Residual Network)	전이 학습
분야	딥러닝	딥러닝	머신러닝
계열	Machine learning	Machine learning	Machine learning
기원 연도	2019	2016	2010 (formalized); 1990s (early roots)
창시자	Tan, M. & Le, Q. V.	He, K.; Zhang, X.; Ren, S.; Sun, J.	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
유형	Compound-scaled convolutional neural network architecture	Deep Convolutional Neural Network with skip connections	Learning paradigm
원전	Tan, M. & Le, Q. V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning (ICML 2019), PMLR 97, 6105–6114.	He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
별칭	EfficientNet, compound scaling CNN, EfficientNet-B0 through B7, EfficientNetV2	ResNet, Residual Network, Deep Residual Learning, ResNet-50	TL, domain adaptation, fine-tuning, pre-trained model adaptation
관련	4	4	3
요약	EfficientNet is a family of convolutional neural network architectures introduced by Mingxing Tan and Quoc V. Le (Google Brain) at ICML 2019 that systematically co-scales network depth, width, and input resolution using a single compound coefficient, achieving state-of-the-art image classification accuracy with substantially fewer parameters and FLOPs than prior networks such as ResNet and Inception.	ResNet (Residual Network) is a deep convolutional neural network architecture introduced by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun at CVPR 2016. By inserting shortcut (skip) connections that carry the input of a block directly to its output — defining the block's task as learning a residual correction rather than a full mapping — ResNet enabled training of networks with hundreds or even thousands of layers without the vanishing-gradient degradation that had previously made very deep networks impractical. It won the ILSVRC 2015 image recognition competition with a top-5 error of 3.57% and remains the most widely used backbone architecture in computer vision.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.