论文标题
retirefievegan：图像合成通过可区分的补丁检索
RetrieveGAN: Image Synthesis via Differentiable Patch Retrieval
论文作者
论文摘要
场景描述中的图像生成是对受控生成的基石技术，这对诸如内容创建和图像编辑之类的应用程序有益。在这项工作中，我们旨在将图像从场景描述中综合图像作为参考。我们建议一个可区分的检索模块。使用可区分的检索模块，我们可以（1）使整个管道端到端训练，从而可以学习更好的功能嵌入以进行检索； （2）鼓励与其他目标函数一起选择相互兼容的斑块。我们进行了广泛的定量和定性实验，以证明所提出的方法可以产生逼真的和多样化的图像，其中检索到的斑块是合理且相互兼容的。
Image generation from scene description is a cornerstone technique for the controlled generation, which is beneficial to applications such as content creation and image editing. In this work, we aim to synthesize images from scene description with retrieved patches as reference. We propose a differentiable retrieval module. With the differentiable retrieval module, we can (1) make the entire pipeline end-to-end trainable, enabling the learning of better feature embedding for retrieval; (2) encourage the selection of mutually compatible patches with additional objective functions. We conduct extensive quantitative and qualitative experiments to demonstrate that the proposed method can generate realistic and diverse images, where the retrieved patches are reasonable and mutually compatible.
