Model-based deep learning methods such as \emph{loop unrolling} (LU) and
\emph{deep equilibrium model} (DEQ) extensions offer outstanding performance in
solving inverse problems (IP). These methods unroll the optimization iterations
into a sequence of neural networks that in effect learn a regularization
function from data. While these architectures are currently state-of-the-art in
numerous applications, their success heavily relies on the accuracy of the
forward model. This assumption can be limiting in many physical applications
due to model simplifications or uncertainties in the apparatus. To address
forward model mismatch, we introduce an untrained forward model residual block
within the model-based architecture to match the data consistency in the
measurement domain for each instance. We propose two variants in well-known
model-based architectures (LU and DEQ) and prove convergence under mild
conditions. The experiments show significant quality improvement in removing
artifacts and preserving details across three distinct applications,
encompassing both linear and nonlinear inverse problems. Moreover, we highlight
reconstruction effectiveness in intermediate steps and showcase robustness to
random initialization of the residual block and a higher number of iterations
during evaluation.

基于模型的深度学习方法在解决逆问题方面表现出色，然而前向模型的准确性对于物理应用来说是有限制的。为了解决这个问题，我们在基于模型的架构中引入一个未训练的前向模型残差块来匹配每个实例在测量域中的数据一致性，我们提出了两个变体，并证明了在温和条件下的收敛性。实验结果显示，在去除伪影并保留细节方面，我们的方法在三个不同应用中都取得了显著的质量改进，涵盖了线性和非线性逆问题。此外，我们突出了中间步骤的重建效果，并展示了对于残差块的随机初始化和更多迭代次数在评估中的稳健性。

利用基于模型的结构中未训练的神经网络解决具有模型不匹配的逆问题

Solving Inverse Problems with Model Mismatch using Untrained Neural  Networks within Model-based Architectures

Generative adversarial networks (GANs), built with a generator and
discriminator, significantly have advanced image generation. Typically,
existing papers build their generators by stacking up multiple residual blocks
since it makes ease the training of generators. However, some recent papers
commented on the limitation of the residual block and proposed a new
architectural unit that improves the GANs performance. Following this trend,
this paper presents a novel unit, called feature cycling block (FCB), which
achieves impressive results in the image generation task. Specifically, the FCB
has two branches: one is a memory branch and the other is an image branch. The
memory branch keeps meaningful information at each stage of the generator,
whereas the image branch takes some useful features from the memory branch to
produce a high-quality image. To show the capability of the proposed method, we
conducted extensive experiments using various datasets including CIFAR-10,
CIFAR-100, FFHQ, AFHQ, and subsets of LSUN. Experimental results demonstrate
the substantial superiority of our approach over the baseline without incurring
any objective functions or training skills. For instance, the proposed method
improves Frechet inception distance (FID) of StyleGAN2 from 4.89 to 3.72 on the
FFHQ dataset and from 6.64 to 5.57 on the LSUN Bed dataset. We believe that the
pioneering attempt presented in this paper could inspire the community with
better-designed generator architecture and with training objectives or skills
compatible with the proposed method.

本研究提出了一种新的单元结构 - 特征循环块（FCB），可以用于生成对抗网络（GANs）的生成器架构中。该单元结构具有两个分支，一个是内存分支，另一个是图像分支。 其中，内存分支在生成器的每个阶段保持有意义的信息，图像分支则从内存分支获取一些有用的特征，以生成高质量的图像。 通过在多个数据集上进行实验，结果表明了本方法的显著优越性。

利用特征循环生成器改进 GANs

Improving GANs with a Feature Cycling Generator

Runtime and memory consumption are two important aspects for efficient image
super-resolution (EISR) models to be deployed on resource-constrained devices.
Recent advances in EISR exploit distillation and aggregation strategies with
plenty of channel split and concatenation operations to make full use of
limited hierarchical features. In contrast, sequential network operations avoid
frequently accessing preceding states and extra nodes, and thus are beneficial
to reducing the memory consumption and runtime overhead. Following this idea,
we design our lightweight network backbone by mainly stacking multiple highly
optimized convolution and activation layers and decreasing the usage of feature
fusion. We propose a novel sequential attention branch, where every pixel is
assigned an important factor according to local and global contexts, to enhance
high-frequency details. In addition, we tailor the residual block for EISR and
propose an enhanced residual block (ERB) to further accelerate the network
inference. Finally, combining all the above techniques, we construct a fast and
memory-efficient network (FMEN) and its small version FMEN-S, which runs 33%
faster and reduces 74% memory consumption compared with the state-of-the-art
EISR model: E-RFDN, the champion in AIM 2020 efficient super-resolution
challenge. Besides, FMEN-S achieves the lowest memory consumption and the
second shortest runtime in NTIRE 2022 challenge on efficient super-resolution.
Code is available at this https URL

提出透過堆疊高度優化的卷積和激活層以及降低特徵融合的方式，設計了一款適用於資源受限設備的快速且記憶體效率高的網路（FMEN 及其小版本 FMEN-S），並應用於圖像超分辨率研究，包含透過逐像素進行局部和全局內容的考量，強化高頻細節的序列注意力分支等技術，有效達成比當前最先進的 EISR 模型：E-RFDN 更快的運行速度以及減少 74％的記憶體消耗的成果。