The current gold standard in solving image processing and computer vision tasks is using supervised learning of deep neural networks (DNNs), requiring large-scale datasets of input-output pairs. In many scenarios in which the output is an image – e.g., medical image analysis, image denoising, deblurring, super-resolution, dehazing, segmentation and optical flow estimation – the collection of labelled image pairs for training is either time-consuming or limited to simple degradation models. Indeed, there is an increasing body of work targeted at weakly supervised training, accompanied with different unsupervised loss functions. This work dives into the regime of Deep-Energy , a task-driven training approach that substitutes the generic loss with minimization of energy functions using DNNs. Such energy functions, often formulated as a combination of a data-fidelity term along with an application-specific prior, are essentially unsupervised as they do not assume knowledge of the output image. As opposed to classic energy minimization, where computationally-intensive inference is performed for each new image, our network, once trained, can compute the output with a single forward-pass operation. By incorporating application-specific domain knowledge to the loss function, we are able to use real-world images, thus decreasing the dependency on pixel-wise labelled data or synthetic datasets. We demonstrate our approach on three different applications: seeded segmentation, image matting and single image dehazing, showing clear benefits of both speedup and improved accuracy versus the classical energy minimization approach, and competitive performance with respect to fully supervised alternatives.

中文翻译：

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深度能源：任务驱动的深度神经网络训练

当前解决图像处理和计算机视觉任务的金标准是使用深度神经网络（DNN）的监督学习，需要大规模的输入输出对数据集。在输出为图像的许多情况下，例如医学图像分析，图像去噪，去模糊，超分辨率，去雾，分割和光流估计，用于训练的标记图像对的收集既费时又受限于简单的退化模型。确实，针对弱监督训练的工作越来越多，同时伴随着不同的无监督损失功能。这项工作深入探讨了深度能量 ，一种任务驱动的培训方法，该方法使用DNN将通用损失替换为最小化能量函数。通常将这些能量函数公式化为数据保真度术语和特定于应用程序的先验的组合，由于它们不承担输出图像的知识，因此基本上不受监管。与经典的能量最小化相反，在经典的能量最小化中，每个新图像都要进行计算量大的推断，而经过训练的我们的网络可以通过一次正向运算来计算输出。通过将特定于应用程序的领域知识整合到损失函数中，我们能够使用真实世界的图像，从而减少了对像素级标记数据或合成数据集的依赖。我们在三种不同的应用程序上展示了我们的方法：种子分割，图像消光和单图像去雾，