Jitkrittum, W;
Sangkloy, P;
Gondal, MW;
Raj, A;
Hays, J;
Schölkopf, B;
(2019)
Kernel Mean Matching for Content Addressability of GANs.
In: Chaudhuri, Kamalika and Salakhutdinov, Ruslan, (eds.)
Proceedings of the International Conference on Machine Learning.
(pp. pp. 3140-3151).
PMLR: Long Beach, California, USA.
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Abstract
We propose a novel procedure which adds "content-addressability" to any given unconditional implicit model e.g., a generative adversarial network (GAN). The procedure allows users to control the generative process by specifying a set (arbitrary size) of desired examples based on which similar samples are generated from the model. The proposed approach, based on kernel mean matching, is applicable to any generative models which transform latent vectors to samples, and does not require retraining of the model. Experiments on various high-dimensional image generation problems (CelebA-HQ, LSUN bedroom, bridge, tower) show that our approach is able to generate images which are consistent with the input set, while retaining the image quality of the original model. To our knowledge, this is the first work that attempts to construct, at test time, a content-addressable generative model from a trained marginal model.
| Type: | Proceedings paper |
|---|---|
| Title: | Kernel Mean Matching for Content Addressability of GANs |
| Event: | International Conference on Machine Learning 2019 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | http://proceedings.mlr.press/v97/jitkrittum19a/jit... |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10091897 |
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