Diffusion Models (DMs) have emerged as powerful generative models with
unprecedented image generation capability. These models are widely used for
data augmentation and creative applications. However, DMs reflect the biases
present in the training datasets. This is especially concerning in the context
of faces, where the DM prefers one demographic subgroup vs others (eg. female
vs male). In this work, we present a method for debiasing DMs without relying
on additional data or model retraining. Specifically, we propose Distribution
Guidance, which enforces the generated images to follow the prescribed
attribute distribution. To realize this, we build on the key insight that the
latent features of denoising UNet hold rich demographic semantics, and the same
can be leveraged to guide debiased generation. We train Attribute Distribution
Predictor (ADP) - a small mlp that maps the latent features to the distribution
of attributes. ADP is trained with pseudo labels generated from existing
attribute classifiers. The proposed Distribution Guidance with ADP enables us
to do fair generation. Our method reduces bias across single/multiple
attributes and outperforms the baseline by a significant margin for
unconditional and text-conditional diffusion models. Further, we present a
downstream task of training a fair attribute classifier by rebalancing the
training set with our generated data.

提出了一种无需额外数据或模型重新训练的去偏置方法，利用属性分布引导生成的图像，通过 Attribute Distribution Predictor（ADP）建立属性分布预测器，从而实现公平生成，并通过生成数据平衡训练集来训练公平属性分类器。

平衡行为：扩散模型中的分布导向去偏

Balancing Act: Distribution-Guided Debiasing in Diffusion Models

Most existing works on fairness assume the model has full access to
demographic information. However, there exist scenarios where demographic
information is partially available because a record was not maintained
throughout data collection or due to privacy reasons. This setting is known as
demographic scarce regime. Prior research have shown that training an attribute
classifier to replace the missing sensitive attributes (proxy) can still
improve fairness. However, the use of proxy-sensitive attributes worsens
fairness-accuracy trade-offs compared to true sensitive attributes. To address
this limitation, we propose a framework to build attribute classifiers that
achieve better fairness-accuracy trade-offs. Our method introduces uncertainty
awareness in the attribute classifier and enforces fairness on samples with
demographic information inferred with the lowest uncertainty. We show
empirically that enforcing fairness constraints on samples with uncertain
sensitive attributes is detrimental to fairness and accuracy. Our experiments
on two datasets showed that the proposed framework yields models with
significantly better fairness-accuracy trade-offs compared to classic attribute
classifiers. Surprisingly, our framework outperforms models trained with
constraints on the true sensitive attributes.

本研究通过引入不确定性感知，对可能存在不确定性的人口敏感属性样本实施公平性要求，并提出一种建立更好的公平性 - 准确性权衡的属性分类器框架，实验证明该框架相对于传统方法在两个数据集上都取得显著的公平性 - 准确性权衡表现优势，并且超过了对真实敏感属性进行约束训练的模型。

人口有限制度下的公平性

Fairness Under Demographic Scarce Regime

Large transformer-based language models (LMs) trained on huge text corpora
have shown unparalleled generation capabilities. However, controlling
attributes of the generated language (e.g. switching topic or sentiment) is
difficult without modifying the model architecture or fine-tuning on
attribute-specific data and entailing the significant cost of retraining. We
propose a simple alternative: the Plug and Play Language Model (PPLM) for
controllable language generation, which combines a pretrained LM with one or
more simple attribute classifiers that guide text generation without any
further training of the LM. In the canonical scenario we present, the attribute
models are simple classifiers consisting of a user-specified bag of words or a
single learned layer with 100,000 times fewer parameters than the LM. Sampling
entails a forward and backward pass in which gradients from the attribute model
push the LM's hidden activations and thus guide the generation. Model samples
demonstrate control over a range of topics and sentiment styles, and extensive
automated and human annotated evaluations show attribute alignment and fluency.
PPLMs are flexible in that any combination of differentiable attribute models
may be used to steer text generation, which will allow for diverse and creative
applications beyond the examples given in this paper.

该研究提出了一种简单的方法 —— 插拔式语言模型（PPLM），结合预训练语言模型和一个或多个简单的属性分类器来进行可控语言生成，其通过属性模型推动预训练语言模型的隐层激活，从而实现对话题和情感样式的控制。