The fairness of AI decision-making has garnered increasing attention, leading
to the proposal of numerous fairness algorithms. In this paper, we aim not to
address this issue by directly introducing fair learning algorithms, but rather
by generating entirely new, fair synthetic data from biased datasets for use in
any downstream tasks. Additionally, the distribution of test data may differ
from that of the training set, potentially impacting the performance of the
generated synthetic data in downstream tasks. To address these two challenges,
we propose a diffusion model-based framework, FADM: Fairness-Aware Diffusion
with Meta-training. FADM introduces two types of gradient induction during the
sampling phase of the diffusion model: one to ensure that the generated samples
belong to the desired target categories, and another to make the sensitive
attributes of the generated samples difficult to classify into any specific
sensitive attribute category. To overcome data distribution shifts in the test
environment, we train the diffusion model and the two classifiers used for
induction within a meta-learning framework. Compared to other baselines, FADM
allows for flexible control over the categories of the generated samples and
exhibits superior generalization capability. Experiments on real datasets
demonstrate that FADM achieves better accuracy and optimal fairness in
downstream tasks.

通过使用扩散模型为基础的框架 FADM，本文提出了一种从存在偏见的数据集中生成全新的、公平的合成数据，而不是直接引入公平学习算法来解决人工智能决策公正性问题。实验证明，FADM 在下游任务中表现出更好的准确性和最佳的公平性，同时允许对生成样本的类别进行灵活控制。

基于评分的扩散模型的公平数据生成

Fair Data Generation via Score-based Diffusion Model

In this paper, we present a diffusion model-based framework for animating
people from a single image for a given target 3D motion sequence. Our approach
has two core components: a) learning priors about invisible parts of the human
body and clothing, and b) rendering novel body poses with proper clothing and
texture. For the first part, we learn an in-filling diffusion model to
hallucinate unseen parts of a person given a single image. We train this model
on texture map space, which makes it more sample-efficient since it is
invariant to pose and viewpoint. Second, we develop a diffusion-based rendering
pipeline, which is controlled by 3D human poses. This produces realistic
renderings of novel poses of the person, including clothing, hair, and
plausible in-filling of unseen regions. This disentangled approach allows our
method to generate a sequence of images that are faithful to the target motion
in the 3D pose and, to the input image in terms of visual similarity. In
addition to that, the 3D control allows various synthetic camera trajectories
to render a person. Our experiments show that our method is resilient in
generating prolonged motions and varied challenging and complex poses compared
to prior methods. Please check our website for more details:
this https URL

利用扩散模型为给定目标 3D 动作序列中的人物从单个图像创建动画的框架，包括学习关于人体和服装不可见部分的先验知识以及呈现适当的身体姿势和纹理的新姿态。