This work aims at transferring a Generative Adversarial Network (GAN)
pre-trained on one image domain to a new domain referring to as few as just one
target image. The main challenge is that, under limited supervision, it is
extremely difficult to synthesize photo-realistic and highly diverse images,
while acquiring representative characters of the target. Different from
existing approaches that adopt the vanilla fine-tuning strategy, we import two
lightweight modules to the generator and the discriminator respectively.
Concretely, we introduce an attribute adaptor into the generator yet freeze its
original parameters, through which it can reuse the prior knowledge to the most
extent and hence maintain the synthesis quality and diversity. We then equip
the well-learned discriminator backbone with an attribute classifier to ensure
that the generator captures the appropriate characters from the reference.
Furthermore, considering the poor diversity of the training data (i.e., as few
as only one image), we propose to also constrain the diversity of the
generative domain in the training process, alleviating the optimization
difficulty. Our approach brings appealing results under various settings,
substantially surpassing state-of-the-art alternatives, especially in terms of
synthesis diversity. Noticeably, our method works well even with large domain
gaps, and robustly converges within a few minutes for each experiment.

研究如何转移使用 GAN 训练的一个图像领域到一个新的领域，只需一个目标图像；通过使用属性适配器和属性分类器的轻量级模块，对生成器和鉴别器进行改进，从而提高合成质量和多样性，并在训练过程中约束生成领域的多样性，不仅在各种设置下带来了具有吸引力的结果，而且能够在几分钟内提供稳健的收敛。

单次生成对抗网络域自适应

One-Shot Generative Domain Adaptation

Training and evaluation of fair classifiers is a challenging problem. This is
partly due to the fact that most fairness metrics of interest depend on both
the sensitive attribute information and label information of the data points.
In many scenarios it is not possible to collect large datasets with such
information. An alternate approach that is commonly used is to separately train
an attribute classifier on data with sensitive attribute information, and then
use it later in the ML pipeline to evaluate the bias of a given classifier.
While such decoupling helps alleviate the problem of demographic scarcity, it
raises several natural questions such as: how should the attribute classifier
be trained?, and how should one use a given attribute classifier for accurate
bias estimation? In this work we study this question from both theoretical and
empirical perspectives.
We first experimentally demonstrate that the test accuracy of the attribute
classifier is not always correlated with its effectiveness in bias estimation
for a downstream model. In order to further investigate this phenomenon, we
analyze an idealized theoretical model and characterize the structure of the
optimal classifier. Our analysis has surprising and counter-intuitive
implications where in certain regimes one might want to distribute the error of
the attribute classifier as unevenly as possible among the different subgroups.
Based on our analysis we develop heuristics for both training and using
attribute classifiers for bias estimation in the data scarce regime. We
empirically demonstrate the effectiveness of our approach on real and simulated
data.

研究公平分类器培训和评估的挑战，探讨对数据点的敏感属性信息和标签信息的公平性指标依赖性，以及在数据缺乏的情况下如何训练和使用属性分类器进行偏差估计。

评估不确定和不完整信息下机器学习模型的公平性

Evaluating Fairness of Machine Learning Models Under Uncertain and  Incomplete Information

Recent research has established the possibility of deducing soft-biometric
attributes such as age, gender and race from an individual's face image with
high accuracy. However, this raises privacy concerns, especially when face
images collected for biometric recognition purposes are used for attribute
analysis without the person's consent. To address this problem, we develop a
technique for imparting soft biometric privacy to face images via an image
perturbation methodology. The image perturbation is undertaken using a
GAN-based Semi-Adversarial Network (SAN) - referred to as PrivacyNet - that
modifies an input face image such that it can be used by a face matcher for
matching purposes but cannot be reliably used by an attribute classifier.
Further, PrivacyNet allows a person to choose specific attributes that have to
be obfuscated in the input face images (e.g., age and race), while allowing for
other types of attributes to be extracted (e.g., gender). Extensive experiments
using multiple face matchers, multiple age/gender/race classifiers, and
multiple face datasets demonstrate the generalizability of the proposed
multi-attribute privacy enhancing method across multiple face and attribute
classifiers.

通过使用基于 GAN 的半对抗网络（PrivacyNet）的图像扰动方法，我们很好地实现了对输入面部图像进行软生物特征隐私的保护，在面部图像用于匹配目的时仍能够进行匹配，但无法可靠地被用于属性分类器。