Dynamic-Net: Tuning the Objective Without Re-Training for Synthesis Tasks

Alon Shoshan, Roey Mechrez, Lihi Zelnik-Manor; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 3215-3223

Abstract


One of the key ingredients for successful optimization of modern CNNs is identifying a suitable objective. To date, the objective is fixed a-priori at training time, and any variation to it requires re-training a new network. In this paper we present a first attempt at alleviating the need for re-training. Rather than fixing the network at training time, we train a "Dynamic-Net" that can be modified at inference time. Our approach considers an "objective-space" as the space of all linear combinations of two objectives, and the Dynamic-Net is emulating the traversing of this objective-space at test-time, without any further training. We show that this upgrades pre-trained networks by providing an out-of-learning extension, while maintaining the performance quality. The solution we propose is fast and allows a user to interactively modify the network, in real-time, in order to obtain the result he/she desires. We show the benefits of such an approach via several different applications.

Related Material


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[bibtex]
@InProceedings{Shoshan_2019_ICCV,
author = {Shoshan, Alon and Mechrez, Roey and Zelnik-Manor, Lihi},
title = {Dynamic-Net: Tuning the Objective Without Re-Training for Synthesis Tasks},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}