Dynamic-Net: Tuning the Objective Without Re-Training for Synthesis Tasks
Alon Shoshan, Roey Mechrez, Lihi Zelnik-Manor
2019年份
35被引次数
7顶会引用
摘要
We propose an approach that enables traversing the "objective-space", spanned by two different objectives, at test-time, without re-training, as illustrated by the blue dot moving along the blue curve in the plot. This is different from the common practice of training a separate network for each objective, represented by ×'s on the plot. Using a single Dynamic-Net we can tune the level of stylization of an image, monitor completion quality per image, or control facial attributes, all interactively at test-time, without re-training. [Animated figure, please view in Acrobat Reader].
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引用它的顶会 Paper7
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- Parametric Pareto Set Learning for Expensive Multi-Objective OptimizationJi Cheng, Bo Xue, Qingfu ZhangAAAI 2026 · 被引用 1 次
- Controlling the Rain: From Removal to RenderingSiqi Ni, Xueyun Cao, Tao Yue, Xuemei HuCVPR 2021
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