Super-Resolution (SR) is a time-hallowed image processing problem that aims
to improve the quality of a Low-Resolution (LR) sample up to the standard of
its High-Resolution (HR) counterpart. We aim to address this by introducing
Super-Resolution Generator (SuRGe), a fully-convolutional Generative
Adversarial Network (GAN)-based architecture for SR. We show that distinct
convolutional features obtained at increasing depths of a GAN generator can be
optimally combined by a set of learnable convex weights to improve the quality
of generated SR samples. In the process, we employ the Jensen-Shannon and the
Gromov-Wasserstein losses respectively between the SR-HR and LR-SR pairs of
distributions to further aid the generator of SuRGe to better exploit the
available information in an attempt to improve SR. Moreover, we train the
discriminator of SuRGe with the Wasserstein loss with gradient penalty, to
primarily prevent mode collapse. The proposed SuRGe, as an end-to-end GAN
workflow tailor-made for super-resolution, offers improved performance while
maintaining low inference time. The efficacy of SuRGe is substantiated by its
superior performance compared to 18 state-of-the-art contenders on 10 benchmark
datasets.

通过引入 SuRGe，一种基于全卷积生成对抗网络（GAN）的超分辨率生成器，我们展示了通过一组可学习的凸权重优化 GAN 生成器的逐渐增加深度的不同卷积特征，从而提高生成的超分辨率样本的质量。同时，通过使用 Jensen-Shannon 和 Gromov-Wasserstein 损失计算 SR-HR 和 LR-SR 分布之间的距离，进一步帮助 SuRGe 生成器更好地利用可用信息以改进超分辨率。此外，我们使用带有梯度惩罚的 Wasserstein 损失训练 SuRGe 的判别器，主要用于防止模态崩溃。与 18 个最先进的竞争模型在 10 个基准数据集上相比，所提出的 SuRGe 以其卓越性能得到证实。

基于散度度量加强的全卷积生成对抗网络在图像超分辨率中的应用

Fortifying Fully Convolutional Generative Adversarial Networks for Image  Super-Resolution Using Divergence Measures

Language models have achieved impressive performances on dialogue generation
tasks. However, when generating responses for a conversation that requires
factual knowledge, they are far from perfect, due to an absence of mechanisms
to retrieve, encode, and reflect the knowledge in the generated responses. Some
knowledge-grounded dialogue generation methods tackle this problem by
leveraging facts from Knowledge Graphs (KGs); however, they do not guarantee
that the model utilizes a relevant piece of knowledge from the KG. To overcome
this limitation, we propose SUbgraph Retrieval-augmented GEneration (SURGE), a
framework for generating context-relevant and knowledge-grounded dialogues with
the KG. Specifically, our SURGE framework first retrieves the relevant subgraph
from the KG, and then enforces consistency across facts by perturbing their
word embeddings conditioned by the retrieved subgraph. Then, we utilize
contrastive learning to ensure that the generated texts have high similarity to
the retrieved subgraphs. We validate our SURGE framework on OpendialKG and
KOMODIS datasets, showing that it generates high-quality dialogues that
faithfully reflect the knowledge from KG.

提出了 SURGE 框架，用于生成上下文相关且基于知识图谱的对话，通过从知识图谱检索相关子图，并对其条件下的单词嵌入进行干扰以保持事实一致性并使用对比学习确保所生成的文本与检索到的子图具有高相似度，通过在 OpendialKG 和 KOMODIS 数据集上的实验验证了该框架的有效性。