Abstract
We propose Deep Feature Interpolation (DFI), a new datadriven baseline for automatic high-resolution image transformation. As the name suggests, DFI 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 after the advent of deep learning