Machine learning
神经风格迁移
神经风格迁移（Neural Style Transfer, NST）是一种深度学习图像合成技术，由Gatys、Ecker和Bethge于2015年提出。该技术将一张图像的语义内容与另一张图像的视觉纹理和艺术风格分离，然后通过迭代优化像素值，将它们重新组合成一张合成图像。优化过程旨在最小化一个结合了内容损失和风格损失的复合损失函数，该损失函数通过预训练卷积神经网络的特征图计算得出。
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来源
- Gatys, L. A., Ecker, A. S., & Bethge, M. (2016). Image Style Transfer Using Convolutional Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2414–2423. DOI: 10.1109/CVPR.2016.265 ↗
- Gatys, L. A., Ecker, A. S., & Bethge, M. (2015). A Neural Algorithm of Artistic Style. arXiv preprint arXiv:1508.06576. link ↗
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. ISBN: 978-0-262-03561-3
如何引用本页
ScholarGate. (2026, June 3). Neural Style Transfer via Convolutional Neural Network Feature Statistics. ScholarGate. https://scholargate.app/zh/deep-learning/neural-style-transfer
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