Semantic Image Synthesis (SIS) is a subclass of image-to-image translation
where a semantic layout is used to generate a photorealistic image.
State-of-the-art conditional Generative Adversarial Networks (GANs) need a huge
amount of paired data to accomplish this task while generic unpaired
image-to-image translation frameworks underperform in comparison, because they
color-code semantic layouts and learn correspondences in appearance instead of
semantic content. Starting from the assumption that a high quality generated
image should be segmented back to its semantic layout, we propose a new
Unsupervised paradigm for SIS (USIS) that makes use of a self-supervised
segmentation loss and whole image wavelet based discrimination. Furthermore, in
order to match the high-frequency distribution of real images, a novel
generator architecture in the wavelet domain is proposed. We test our
methodology on 3 challenging datasets and demonstrate its ability to bridge the
performance gap between paired and unpaired models.

本文提出了一种新的无监督学习范例 (Unsupervised Paradigm for SIS, USIS)，通过使用自监督分割损失和基于整个图像小波的鉴别方法，结合在小波域中的生成器结构，使得语义图像合成 (SIS) 在不需要大量的配对数据的情况下实现，从而弥补了配对和非配对模型之间的性能差距。在三个具有挑战性的数据集上进行测试，证明了新方法的有效性。

基于小波的无监督标签到图像转换

Wavelet-based Unsupervised Label-to-Image Translation

A long-standing issue with paraphrase generation is how to obtain reliable
supervision signals. In this paper, we propose an unsupervised paradigm for
paraphrase generation based on the assumption that the probabilities of
generating two sentences with the same meaning given the same context should be
the same. Inspired by this fundamental idea, we propose a pipelined system
which consists of paraphrase candidate generation based on contextual language
models, candidate filtering using scoring functions, and paraphrase model
training based on the selected candidates. The proposed paradigm offers merits
over existing paraphrase generation methods: (1) using the context regularizer
on meanings, the model is able to generate massive amounts of high-quality
paraphrase pairs; and (2) using human-interpretable scoring functions to select
paraphrase pairs from candidates, the proposed framework provides a channel for
developers to intervene with the data generation process, leading to a more
controllable model. Experimental results across different tasks and datasets
demonstrate that the effectiveness of the proposed model in both supervised and
unsupervised setups.

本文提出了一种基于无监督范式的释义生成方法，思路基于生成相同含义的两个句子的概率应该相同，提出了一种由基于上下文语言模型生成释义候选项、使用评分函数进行候选项过滤、基于筛选的候选项进行释义模型训练的管道系统，并在不同任务和数据集上进行实验，表明该方法在有监督和无监督设置中均有效。