Text-to-image generative AI models such as Stable Diffusion are used daily by
millions worldwide. However, many have raised concerns regarding how these
models amplify racial and gender stereotypes. To study this phenomenon, we
develop a classifier to predict the race, gender, and age group of any given
face image, and show that it achieves state-of-the-art performance. Using this
classifier, we quantify biases in Stable Diffusion across six races, two
genders, five age groups, 32 professions, and eight attributes. We then propose
novel debiasing solutions that outperform state-of-the-art alternatives.
Additionally, we examine the degree to which Stable Diffusion depicts
individuals of the same race as being similar to one another. This analysis
reveals a high degree of stereotyping, e.g., depicting most middle eastern
males as being dark-skinned, bearded, and wearing a traditional headdress. We
address these limitations by proposing yet another novel solution that
increases facial diversity across genders and racial groups. Our solutions are
open-sourced and made publicly available.

使用分类器研究文中文字图生成 AI 模型中的种族和性别刻板印象，提出一种新的去偏方法并增加人脸多样性。

摆脱种族和性别刻板印象的人工智能生成的人脸

AI-generated faces free from racial and gender stereotypes

Scene Graph Generation (SGG) aims to detect all the visual relation triplets
<sub, pred, obj> in a given image. With the emergence of various advanced
techniques for better utilizing both the intrinsic and extrinsic information in
each relation triplet, SGG has achieved great progress over the recent years.
However, due to the ubiquitous long-tailed predicate distributions, today's SGG
models are still easily biased to the head predicates. Currently, the most
prevalent debiasing solutions for SGG are re-balancing methods, e.g., changing
the distributions of original training samples. In this paper, we argue that
all existing re-balancing strategies fail to increase the diversity of the
relation triplet features of each predicate, which is critical for robust SGG.
To this end, we propose a novel Compositional Feature Augmentation (CFA)
strategy, which is the first unbiased SGG work to mitigate the bias issue from
the perspective of increasing the diversity of triplet features. Specifically,
we first decompose each relation triplet feature into two components: intrinsic
feature and extrinsic feature, which correspond to the intrinsic
characteristics and extrinsic contexts of a relation triplet, respectively.
Then, we design two different feature augmentation modules to enrich the
feature diversity of original relation triplets by replacing or mixing up
either their intrinsic or extrinsic features from other samples. Due to its
model-agnostic nature, CFA can be seamlessly incorporated into various SGG
frameworks. Extensive ablations have shown that CFA achieves a new
state-of-the-art performance on the trade-off between different metrics.

我们提出了一种新颖的组合特征增强（CFA）策略，通过增加三元组特征的多样性来解决现有的偏差问题，该方法在各种场景图生成框架中都能无缝集成，并在不同指标之间取得了最新的最优性能。

无偏场景图生成的组合特征增强

Compositional Feature Augmentation for Unbiased Scene Graph Generation

Most recommender systems optimize the model on observed interaction data,
which is affected by the previous exposure mechanism and exhibits many biases
like popularity bias. The loss functions, such as the mostly used pointwise
Binary Cross-Entropy and pairwise Bayesian Personalized Ranking, are not
designed to consider the biases in observed data. As a result, the model
optimized on the loss would inherit the data biases, or even worse, amplify the
biases. For example, a few popular items take up more and more exposure
opportunities, severely hurting the recommendation quality on niche items --
known as the notorious Mathew effect. In this work, we develop a new learning
paradigm named Cross Pairwise Ranking (CPR) that achieves unbiased
recommendation without knowing the exposure mechanism. Distinct from inverse
propensity scoring (IPS), we change the loss term of a sample -- we
innovatively sample multiple observed interactions once and form the loss as
the combination of their predictions. We prove in theory that this way offsets
the influence of user/item propensity on the learning, removing the influence
of data biases caused by the exposure mechanism. Advantageous to IPS, our
proposed CPR ensures unbiased learning for each training instance without the
need of setting the propensity scores. Experimental results demonstrate the
superiority of CPR over state-of-the-art debiasing solutions in both model
generalization and training efficiency. The codes are available at
this https URL

本文提出了一个名为交叉成对排序（CPR）的新学习范式，它在不了解暴露机制的情况下实现了无偏推荐，相比现有的去偏方法具有更好的模型泛化和训练效率。