The absence of real targets to guide the model training is one of the main
problems with the makeup transfer task. Most existing methods tackle this
problem by synthesizing pseudo ground truths (PGTs). However, the generated
PGTs are often sub-optimal and their imprecision will eventually lead to
performance degradation. To alleviate this issue, in this paper, we propose a
novel Content-Style Decoupled Makeup Transfer (CSD-MT) method, which works in a
purely unsupervised manner and thus eliminates the negative effects of
generating PGTs. Specifically, based on the frequency characteristics analysis,
we assume that the low-frequency (LF) component of a face image is more
associated with its makeup style information, while the high-frequency (HF)
component is more related to its content details. This assumption allows CSD-MT
to decouple the content and makeup style information in each face image through
the frequency decomposition. After that, CSD-MT realizes makeup transfer by
maximizing the consistency of these two types of information between the
transferred result and input images, respectively. Two newly designed loss
functions are also introduced to further improve the transfer performance.
Extensive quantitative and qualitative analyses show the effectiveness of our
CSD-MT method. Our code is available at
this https URL

通过频率特征分析，本文提出了一种以纯无监督方式工作的内容 - 风格分离化妆转换方法（CSD-MT），通过频率分解来解耦每个面部图像中的内容和化妆风格信息，进而实现化妆转换，并通过引入两种新设计的损失函数进一步提高转换性能。

无生成伪造真值进行的无监督化妆迁移的内容风格分离

Content-Style Decoupling for Unsupervised Makeup Transfer without  Generating Pseudo Ground Truth

In this paper, we propose an effective knowledge transfer framework to boost
the weakly supervised object detection accuracy with the help of an external
fully-annotated source dataset, whose categories may not overlap with the
target domain. This setting is of great practical value due to the existence of
many off-the-shelf detection datasets. To more effectively utilize the source
dataset, we propose to iteratively transfer the knowledge from the source
domain by a one-class universal detector and learn the target-domain detector.
The box-level pseudo ground truths mined by the target-domain detector in each
iteration effectively improve the one-class universal detector. Therefore, the
knowledge in the source dataset is more thoroughly exploited and leveraged.
Extensive experiments are conducted with Pascal VOC 2007 as the target
weakly-annotated dataset and COCO/ImageNet as the source fully-annotated
dataset. With the proposed solution, we achieved an mAP of $59.7\%$ detection
performance on the VOC test set and an mAP of $60.2\%$ after retraining a fully
supervised Faster RCNN with the mined pseudo ground truths. This is
significantly better than any previously known results in related literature
and sets a new state-of-the-art of weakly supervised object detection under the
knowledge transfer setting. Code:
https://github.com/mikuhatsune/wsod_transfer.

本文提出了一种有效的知识转移框架，利用一个在类别上与目标领域不重叠的外部全注释源数据集，迭代地转移源域知识，通过一个单类通用探测器学习目标域的探测器，并从中挖掘每次迭代中目标域探测器挖掘的伪实例，有效提高了单类通用探测器，从而更全面地利用和利用源数据集的知识，该方法在弱监督目标检测领域内实现了先前未知的最佳效果。