  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.
