Image translation methods typically aim to manipulate a set of labeled
attributes (given as supervision at training time e.g. domain label) while
leaving the unlabeled attributes intact. Current methods achieve either: (i)
disentanglement, which exhibits low visual fidelity and can only be satisfied
where the attributes are perfectly uncorrelated. (ii) visually-plausible
translations, which are clearly not disentangled. In this work, we propose
OverLORD, a single framework for disentangling labeled and unlabeled attributes
as well as synthesizing high-fidelity images, which is composed of two stages;
(i) Disentanglement: Learning disentangled representations with latent
optimization. Differently from previous approaches, we do not rely on
adversarial training or any architectural biases. (ii) Synthesis: Training
feed-forward encoders for inferring the learned attributes and tuning the
generator in an adversarial manner to increase the perceptual quality. When the
labeled and unlabeled attributes are correlated, we model an additional
representation that accounts for the correlated attributes and improves
disentanglement. We highlight that our flexible framework covers multiple
settings as disentangling labeled attributes, pose and appearance, localized
concepts, and shape and texture. We present significantly better
disentanglement with higher translation quality and greater output diversity
than state-of-the-art methods.

本文提出一种名为 OverLORD 的单一框架，用于解缠标记和未标记的属性，以及合成高保真度图像，并通过潜在的优化实现分离学习，该方法不依赖对抗性训练或任何结构偏差，并在生成更具感知品质的图片中具有显着更好的解离和更高的输出多样性.

图像翻译中的解缠缚技术扩展

Scaling-up Disentanglement for Image Translation

Caricature attributes provide distinctive facial features to help research in
Psychology and Neuroscience. However, unlike the facial photo attribute
datasets that have a quantity of annotated images, the annotations of
caricature attributes are rare. To facility the research in attribute learning
of caricatures, we propose a caricature attribute dataset, namely WebCariA.
Moreover, to utilize models that trained by face attributes, we propose a novel
unsupervised domain adaptation framework for cross-modality (i.e., photos to
caricatures) attribute recognition, with an integrated inter- and intra-domain
consistency learning scheme. Specifically, the inter-domain consistency
learning scheme consisting an image-to-image translator to first fill the
domain gap between photos and caricatures by generating intermediate image
samples, and a label consistency learning module to align their semantic
information. The intra-domain consistency learning scheme integrates the common
feature consistency learning module with a novel attribute-aware
attention-consistency learning module for a more efficient alignment. We did an
extensive ablation study to show the effectiveness of the proposed method. And
the proposed method also outperforms the state-of-the-art methods by a margin.
The implementation of the proposed method is available at
this https URL

本研究提出了一个新的 dataset 和跨模态的无监督域适应框架，用于学习卡通肖像画的属性特征，该方法通过图像翻译器和标签一致性学习模块对图像和语义信息进行对齐，同时利用常见特征和属性感知的 attention-consistency 模块实现更高效的对齐。实验表明该方法的有效性和优越性。

面向漫画属性识别的无监督领域关注自适应网络

Unsupervised Domain Attention Adaptation Network for Caricature  Attribute Recognition

Traditionally, most of the existing attribute learning methods are trained
based on the consensus of annotations aggregated from a limited number of
annotators. However, the consensus might fail in settings, especially when a
wide spectrum of annotators with different interests and comprehension about
the attribute words are involved. In this paper, we develop a novel multi-task
method to understand and predict personalized attribute annotations. Regarding
the attribute preference learning for each annotator as a specific task, we
first propose a multi-level task parameter decomposition to capture the
evolution from a highly popular opinion of the mass to highly personalized
choices that are special for each person. Meanwhile, for personalized learning
methods, ranking prediction is much more important than accurate
classification. This motivates us to employ an Area Under ROC Curve (AUC) based
loss function to improve our model. On top of the AUC-based loss, we propose an
efficient method to evaluate the loss and gradients. Theoretically, we propose
a novel closed-form solution for one of our non-convex subproblem, which leads
to provable convergence behaviors. Furthermore, we also provide a
generalization bound to guarantee a reasonable performance. Finally, empirical
analysis consistently speaks to the efficacy of our proposed method.

提出一种基于多任务学习的方法，通过属性偏好学习、AUC 损失函数以及梯度的高效计算方法，实现了个性化的属性学习，进而保证个性化的排序预测效果。