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arXiv · 1611.05507

Deep Feature Interpolation for Image Content Changes

Paul Upchurch·Jacob Gardner·Geoff Pleiss·Robert Pless·Noah Snavely·Kavita Bala·Kilian Weinberger
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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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Paul Upchurch, Jacob Gardner, Geoff Pleiss, Robert Pless, Noah Snavely, Kavita Bala, Kilian Weinberger. 2016-11-16. Deep Feature Interpolation for Image Content Changes. https://arxiv.org/abs/1611.05507

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