论文标题
将图像转换为班级生成网络
Transforming and Projecting Images into Class-conditional Generative Networks
论文作者
论文摘要
我们提出了一种将输入图像投射到类别生成神经网络空间中的方法。我们提出了一种优化转换以抵消生成神经网络中模型偏见的方法。具体而言，我们证明了在投影优化期间可以解决图像翻译，比例和全局颜色转换，以解决生成对抗性网络的对象中心偏置和颜色偏差。这种投影过程带来了一个困难的优化问题，纯粹基于梯度的优化无法找到良好的解决方案。我们描述了一种混合优化策略，该策略通过估计转换和类参数来找到良好的预测。我们显示了我们方法对真实图像的有效性，并进一步证明了相应的预测如何导致这些图像的更好编辑性。
We present a method for projecting an input image into the space of a class-conditional generative neural network. We propose a method that optimizes for transformation to counteract the model biases in generative neural networks. Specifically, we demonstrate that one can solve for image translation, scale, and global color transformation, during the projection optimization to address the object-center bias and color bias of a Generative Adversarial Network. This projection process poses a difficult optimization problem, and purely gradient-based optimizations fail to find good solutions. We describe a hybrid optimization strategy that finds good projections by estimating transformations and class parameters. We show the effectiveness of our method on real images and further demonstrate how the corresponding projections lead to better editability of these images.
