非光滑能量耗散网络

Hannah Dröge, T. Möllenhoff, Michael Möller
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引用次数: 0

摘要

在过去的十年中,深度神经网络已经被证明在各种图像重建任务上表现得非常好。然而,这类网络确实无法为这些预测提供保证,这使得它们难以用于安全关键型应用。最近的研究通过结合基于模型和学习的方法来解决这个问题,例如,通过预测合适的下降方向,迫使网络迭代地最小化基于模型的成本函数。以往的方法仅限于连续可微的代价函数,本文讨论了一种消除可微限制的方法。我们建议使用这些成本的Moreau-Yosida正则化来使能量耗散网络框架适用。我们在两个示例应用中展示了我们的框架,即保护能量耗散去噪网络对噪声的预期分布,以及对条形码去模糊网络实施二进制约束以提高其各自的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non-Smooth Energy Dissipating Networks
Over the past decade, deep neural networks have been shown to perform extremely well on a variety of image reconstruction tasks. Such networks do, however, fail to provide guarantees about these predictions, making them difficult to use in safety-critical applications. Recent works addressed this problem by combining model-and learning-based approaches, e.g., by forcing networks to iteratively minimize a model-based cost function via the prediction of suitable descent directions. While previous approaches were limited to continuously differentiable cost functions, this paper discusses a way to remove the restriction of differentiability. We propose to use the Moreau-Yosida regularization of such costs to make the framework of energy dissipating networks applicable. We demonstrate our framework on two exemplary applications, i.e., safeguarding energy dissipating denoising networks to the expected distribution of the noise as well as enforcing binary constraints on bar-code deblurring networks to improve their respective performances.
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