Paul Upchurch1,2,
Jacob Gardner1,2,
Geoff Pleiss2,
Robert Pless3,
Noah Snavely2,
Kavita Bala2,
Kilian Q. Weinberger2
1Authors contributed equally,
2Cornell University,
3George Washington University
CVPR 2017
Download: Paper (9.7 MB), Supplemental (37 MB), Poster (47 MB), Code (GitHub)
Abstract: 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.
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