Object frequency in the real world often follows a power law, leading to a
mismatch between datasets with long-tailed class distributions seen by a
machine learning model and our expectation of the model to perform well on all
classes. We analyze this mismatch from a domain adaptation point of view. First
of all, we connect existing class-balanced methods for long-tailed
classification to target shift, a well-studied scenario in domain adaptation.
The connection reveals that these methods implicitly assume that the training
data and test data share the same class-conditioned distribution, which does
not hold in general and especially for the tail classes. While a head class
could contain abundant and diverse training examples that well represent the
expected data at inference time, the tail classes are often short of
representative training data. To this end, we propose to augment the classic
class-balanced learning by explicitly estimating the differences between the
class-conditioned distributions with a meta-learning approach. We validate our
approach with six benchmark datasets and three loss functions.

本文从领域自适应的角度分析了分类中长尾类别分布的问题，提出了基于元学习的显式估计类别条件分布差异的方案，并在六个基准数据集和三个损失函数上进行了验证。

从域自适应的角度重新思考针对长尾视觉识别的类别平衡方法

Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition  from a Domain Adaptation Perspective

This paper proposes a novel approach for unsupervised domain adaptation (UDA)
with target shift. Target shift is a problem of mismatch in label distribution
between source and target domains. Typically it appears as class-imbalance in
target domain. In practice, this is an important problem in UDA; as we do not
know labels in target domain datasets, we do not know whether or not its
distribution is identical to that in the source domain dataset. Many
traditional approaches achieve UDA with distribution matching by minimizing
mean maximum discrepancy or adversarial training; however these approaches
implicitly assume a coincidence in the distributions and do not work under
situations with target shift. Some recent UDA approaches focus on class
boundary and some of them are robust to target shift, but they are only
applicable to classification and not to regression.
To overcome the target shift problem in UDA, the proposed method, partially
shared variational autoencoders (PS-VAEs), uses pair-wise feature alignment
instead of feature distribution matching. PS-VAEs inter-convert domain of each
sample by a CycleGAN-based architecture while preserving its label-related
content. To evaluate the performance of PS-VAEs, we carried out two
experiments: UDA with class-unbalanced digits datasets (classification), and
UDA from synthesized data to real observation in human-pose-estimation
(regression). The proposed method presented its robustness against the
class-imbalance in the classification task, and outperformed the other methods
in the regression task with a large margin.

本文提出了一种用于无监督域适应的新方法，该方法使用 PS-VAEs 和 CycleGAN 在目标数据集的标签分布与源数据集的标签分布不匹配的情况下进行特征对齐，以解决目标移位问题，并在分类和回归任务中取得了良好的效果。