We introduce a new architecture called a conditional invertible neural network (cINN), and use it to address the task of diverse image-to-image translation for natural images. This is not easily possible with existing INN models due to some fundamental limitations. The cINN combines the purely generative INN model with an unconstrained feed-forward network, which efficiently preprocesses the conditioning image into maximally informative features. All parameters of a cINN are jointly optimized with a stable, maximum likelihood-based training procedure. Even though INN-based models have received far less attention in the literature than GANs, they have been shown to have some remarkable properties absent in GANs, e.g. apparent immunity to mode collapse. We find that our cINNs leverage these properties for image-to-image translation, demonstrated on day to night translation and image colorization. Furthermore, we take advantage of our bidirectional cINN architecture to explore and manipulate emergent properties of the latent space, such as changing the image style in an intuitive way.

中文翻译：

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条件可逆神经网络用于图像到图像的多样翻译

我们介绍了一种称为条件可逆神经网络（cINN）的新体系结构，并将其用于解决自然图像的各种图像到图像转换的任务。由于某些基本限制，使用现有的INN模型很难做到这一点。cINN将纯粹生成的INN模型与不受约束的前馈网络相结合，可将预处理图像有效地预处理为具有最大信息量的特征。cINN的所有参数均通过稳定的，基于最大似然的训练过程共同优化。尽管基于INN的模型在文献中所受到的关注远少于GAN，但已证明它们具有GAN中不具备的一些显着特性，例如对模式崩溃的明显免疫力。我们发现，我们的cINN利用这些属性来进行图像到图像的翻译，在白天到晚上进行演示和图像着色。此外，我们利用双向cINN架构来探索和操纵潜在空间的新兴属性，例如以直观的方式更改图像样式。