We propose Deep Feature Interpolation (DFI), a new data-driven baseline for
automatic high-resolution image transformation. As the name suggests, it relies
only on simple linear interpolation of deep convolutional features from
pre-trained convnets. We show that despite its simplicity, DFI can perform
high-level semantic transformations like "make older/younger", "make
bespectacled", "add smile", among others, surprisingly well - sometimes even
matching or outperforming the state-of-the-art. This is particularly unexpected
as DFI requires no specialized network architecture or even any deep network to
be trained for these tasks. DFI therefore can be used as a new baseline to
evaluate more complex algorithms and provides a practical answer to the
question of which image transformation tasks are still challenging in the rise
of deep learning.

本文提出了 Deep Feature Interpolation（DFI）作为高分辨率图像转换的新数据驱动基准，只依赖于从预训练的卷积神经网络中进行深度卷积特征的简单线性插值。我们发现，虽然 DFI 非常简单，但是可以执行高级语义变换，如 “变老 / 年轻”，“加眼镜”，“加微笑” 等，在某些情况下甚至可以匹配或优于最先进的技术。DFI 因此可以作为评估更复杂算法的新基线，并提供了一个实际的答案，回答了在深度学习的崛起中哪些图像转换任务仍然具有挑战性的问题。