We introduce a novel formulation for guided super-resolution. Its core is a
differentiable optimisation layer that operates on a learned affinity graph.
The learned graph potentials make it possible to leverage rich contextual
information from the guide image, while the explicit graph optimisation within
the architecture guarantees rigorous fidelity of the high-resolution target to
the low-resolution source. With the decision to employ the source as a
constraint rather than only as an input to the prediction, our method differs
from state-of-the-art deep architectures for guided super-resolution, which
produce targets that, when downsampled, will only approximately reproduce the
source. This is not only theoretically appealing, but also produces crisper,
more natural-looking images. A key property of our method is that, although the
graph connectivity is restricted to the pixel lattice, the associated edge
potentials are learned with a deep feature extractor and can encode rich
context information over large receptive fields. By taking advantage of the
sparse graph connectivity, it becomes possible to propagate gradients through
the optimisation layer and learn the edge potentials from data. We extensively
evaluate our method on several datasets, and consistently outperform recent
baselines in terms of quantitative reconstruction errors, while also delivering
visually sharper outputs. Moreover, we demonstrate that our method generalises
particularly well to new datasets not seen during training.

本文提出了基于可微分的优化层和学习的亲和力图形成的新型超分辨率方法，该方法与先前的引导超分辨率深层架构不同，采用源图像作为约束而非仅仅是输入预测。实验表明，该方法在多个数据集上具有很好的效果。

引导式超分辨率图像增强的图正则化学习

Learning Graph Regularisation for Guided Super-Resolution

Motivated by the problem relatedness between unsupervised domain adaptation
(UDA) and semi-supervised learning (SSL), many state-of-the-art UDA methods
adopt SSL principles (e.g., the cluster assumption) as their learning
ingredients. However, they tend to overlook the very domain-shift nature of
UDA. In this work, we take a step further to study the proper extensions of SSL
techniques for UDA. Taking the algorithm of label propagation (LP) as an
example, we analyze the challenges of adopting LP to UDA and theoretically
analyze the conditions of affinity graph/matrix construction in order to
achieve better propagation of true labels to unlabeled instances. Our analysis
suggests a new algorithm of Label Propagation with Augmented Anchors (A$^2$LP),
which could potentially improve LP via generation of unlabeled virtual
instances (i.e., the augmented anchors) with high-confidence label predictions.
To make the proposed A$^2$LP useful for UDA, we propose empirical schemes to
generate such virtual instances. The proposed schemes also tackle the
domain-shift challenge of UDA by alternating between pseudo labeling via
A$^2$LP and domain-invariant feature learning. Experiments show that such a
simple SSL extension improves over representative UDA methods of
domain-invariant feature learning, and could empower two state-of-the-art
methods on benchmark UDA datasets. Our results show the value of further
investigation on SSL techniques for UDA problems.

通过分析 WSL 技术在 UDA 问题中遇到的挑战，提出了一种 Label Propagation with Augmented Anchors (A2LP) 算法，通过生成带有高置信度标签预测的未标记虚拟实例 (即增强锚点) 来改进 LP。经过实验证明，A2LP 作为简单的 WSL 扩展改进了基准 UDA 技术，强化了两种最先进的方法。

使用增强锚点的标签传播算法：一种简单的半监督学习基线用于无监督领域自适应

Label Propagation with Augmented Anchors: A Simple Semi-Supervised  Learning baseline for Unsupervised Domain Adaptation

We describe a computationally efficient, stochastic graph-regularization
technique that can be utilized for the semi-supervised training of deep neural
networks in a parallel or distributed setting. We utilize a technique, first
described in [13] for the construction of mini-batches for stochastic gradient
descent (SGD) based on synthesized partitions of an affinity graph that are
consistent with the graph structure, but also preserve enough stochasticity for
convergence of SGD to good local minima. We show how our technique allows a
graph-based semi-supervised loss function to be decomposed into a sum over
objectives, facilitating data parallelism for scalable training of machine
learning models. Empirical results indicate that our method significantly
improves classification accuracy compared to the fully-supervised case when the
fraction of labeled data is low, and in the parallel case, achieves significant
speed-up in terms of wall-clock time to convergence. We show the results for
both sequential and distributed-memory semi-supervised DNN training on a speech
corpus.

该研究介绍了一种可以在并行或分布式设置中利用的计算效率高和随机图正则化技术，以进行深度神经网络的半监督训练，这种技术基于亲和图的合成分区进行随机梯度下降的小批量构建，证明我们的方法可以让基于图表的半监督损失函数分解成目标求和，从而方便可扩展的机器学习模型训练。实证结果显示，相比完全监督情况，当标记数据比例较低时，我们的方法显著提高了分类准确性，并在并行情况下实现了显著的收敛时间加速。