Dataset bias is a critical challenge in machine learning since it often leads
to a negative impact on a model due to the unintended decision rules captured
by spurious correlations. Although existing works often handle this issue based
on human supervision, the availability of the proper annotations is impractical
and even unrealistic. To better tackle the limitation, we propose a simple but
effective unsupervised debiasing technique. Specifically, we first identify
pseudo-attributes based on the results from clustering performed in the feature
embedding space even without an explicit bias attribute supervision. Then, we
employ a novel cluster-wise reweighting scheme to learn debiased
representation; the proposed method prevents minority groups from being
discounted for minimizing the overall loss, which is desirable for worst-case
generalization. The extensive experiments demonstrate the outstanding
performance of our approach on multiple standard benchmarks, even achieving the
competitive accuracy to the supervised counterpart.

该论文提出了一种简单且有效的无监督去偏差技术，该方法利用聚类算法在特征嵌入空间识别伪属性，然后采用一种新颖的聚类加权重新调整方案来学习去偏置表示，以防止少数群体被忽视并达到最坏情况下的概括，实验证明其在多个标准基准测试数据集上具有出色的表现，甚至达到了有监督对照组的竞争精度。

无监督学习伪属性去偏表示

Unsupervised Learning of Debiased Representations with Pseudo-Attributes

One of the main challenges in Zero-Shot Learning of visual categories is
gathering semantic attributes to accompany images. Recent work has shown that
learning from textual descriptions, such as Wikipedia articles, avoids the
problem of having to explicitly define these attributes. We present a new model
that can classify unseen categories from their textual description.
Specifically, we use text features to predict the output weights of both the
convolutional and the fully connected layers in a deep convolutional neural
network (CNN). We take advantage of the architecture of CNNs and learn features
at different layers, rather than just learning an embedding space for both
modalities, as is common with existing approaches. The proposed model also
allows us to automatically generate a list of pseudo- attributes for each
visual category consisting of words from Wikipedia articles. We train our
models end-to-end us- ing the Caltech-UCSD bird and flower datasets and
evaluate both ROC and Precision-Recall curves. Our empirical results show that
the proposed model significantly outperforms previous methods.

本文提出了一种新的模型，使用文字特征来预测深度卷积神经网络（CNN）中卷积和全连接层的输出权重，以分类看不见的类别，同时利用 CNN 架构在不同层面学习特征，从而避免了为图像定义语义属性的问题，并且自动生成了每个视觉类别的伪属性词列表。结果显示，该模型明显优于以往方法。