Deep Feature Interpolation for Image Content Changes

Paul Upchurch, Jacob Gardner, Geoff Pleiss, Robert Pless, Noah Snavely, Kavita Bala, Kilian Weinberger
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
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
more » ... 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.
doi:10.1109/cvpr.2017.645 dblp:conf/cvpr/UpchurchGPPSBW17 fatcat:7xqscf7ogne2nazr7dbx36f53e