方法证据记录
Neural Style Transfer
Neural Style Transfer (NST) is a deep-learning image synthesis technique, introduced by Gatys, Ecker, and Bethge in 2015, that separates the semantic content of one image from the visual texture and artistic style of another, then recombines them into a single synthesized image by iteratively optimizing pixel values to minimize a combined content and style loss computed from the feature maps of a pretrained convolutional neural network.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Neural Style Transfer via Convolutional Neural Network Feature Statistics
分类方法记录 · ml-model / deep-learning
- 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. · URL
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. · ISBN 978-0-262-03561-3
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