A common problem for composite images is the incompatibility of their
foreground and background components. Image harmonization aims to solve this
problem, making the whole image look more authentic and coherent. Most existing
solutions predict lookup tables (LUTs) or reconstruct images, utilizing various
attributes of composite images. Recent approaches have primarily focused on
employing global transformations like normalization and color curve rendering
to achieve visual consistency, and they often overlook the importance of local
visual coherence. We present a patch-based harmonization network consisting of
novel Patch-based normalization (PN) blocks and a feature extractor based on
statistical color transfer. Extensive experiments demonstrate the network's
high generalization capability for different domains. Our network achieves
state-of-the-art results on the iHarmony4 dataset. Also, we created a new human
portrait harmonization dataset based on FFHQ and checked the proposed method to
show the generalization ability by achieving the best metrics on it. The
benchmark experiments confirm that the suggested patch-based normalization
block and feature extractor effectively improve the network's capability to
harmonize portraits. Our code and model baselines are publicly available.

利用基于补丁的调和网络和基于统计色彩转移的特征提取器实现补丁特征归一化块，并在图像调和中取得优异的结果。

PHNet：基于补丁的人像协调化归一化

PHNet: Patch-based Normalization for Portrait Harmonization

Flamelet models are widely used in computational fluid dynamics to simulate
thermochemical processes in turbulent combustion. These models typically employ
memory-expensive lookup tables that are predetermined and represent the
combustion process to be simulated. Artificial neural networks (ANNs) offer a
deep learning approach that can store this tabular data using a small number of
network weights, potentially reducing the memory demands of complex simulations
by orders of magnitude. However, ANNs with standard training losses often
struggle with underrepresented targets in multivariate regression tasks, e.g.,
when learning minor species mass fractions as part of lookup tables. This paper
seeks to improve the accuracy of an ANN when learning multiple species mass
fractions of a hydrogen (\ce{H2}) combustion lookup table. We assess a simple,
yet effective loss weight adjustment that outperforms the standard mean-squared
error optimization and enables accurate learning of all species mass fractions,
even of minor species where the standard optimization completely fails.
Furthermore, we find that the loss weight adjustment leads to more balanced
gradients in the network training, which explains its effectiveness.

通过简单而有效的损失权重调整方法，提高了人工神经网络在学习多组分质量分数时的准确性，并且带来了更平衡的网络训练梯度。