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One of the key ingredients for successful optimization of modern CNNs isidentifying a suitable objective. To date, the objective is fixed a-priori attraining time, and any variation to it requires re-training a new network. Inthis 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" thatcan be modified at inference time. Our approach considers an "objective-space"as the space of all linear combinations of two objectives, and the Dynamic-Netis emulating the traversing of this objective-space at test-time, without anyfurther training. We show that this upgrades pre-trained networks by providingan out-of-learning extension, while maintaining the performance quality. Thesolution we propose is fast and allows a user to interactively modify thenetwork, in real-time, in order to obtain the result he/she desires. We showthe benefits of such an approach via several different applications.

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