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.
@article{arxiv.1611.05507,
title = {Deep Feature Interpolation for Image Content Changes},
author = {Paul Upchurch and Jacob Gardner and Geoff Pleiss and Robert Pless and Noah Snavely and Kavita Bala and Kilian Weinberger},
journal= {arXiv preprint arXiv:1611.05507},
year = {2017}
}
Comments
First two authors contributed equally. Accepted by CVPR 2017. Code at https://github.com/paulu/deepfeatinterp