Subpopulation shift exists widely in many real-world applications, which
refers to the training and test distributions that contain the same
subpopulation groups but with different subpopulation proportions. Ignoring
subpopulation shifts may lead to significant performance degradation and
fairness concerns. Importance reweighting is a classical and effective way to
handle the subpopulation shift. However, recent studies have recognized that
most of these approaches fail to improve the performance especially when
applied to over-parameterized neural networks which are capable of fitting any
training samples. In this work, we propose a simple yet practical framework,
called reweighted mixup (RMIX), to mitigate the overfitting issue in
over-parameterized models by conducting importance weighting on the ''mixed''
samples. Benefiting from leveraging reweighting in mixup, RMIX allows the model
to explore the vicinal space of minority samples more, thereby obtaining more
robust model against subpopulation shift. When the subpopulation memberships
are unknown, the training-trajectories-based uncertainty estimation is equipped
in the proposed RMIX to flexibly characterize the subpopulation distribution.
We also provide insightful theoretical analysis to verify that RMIX achieves
better generalization bounds over prior works. Further, we conduct extensive
empirical studies across a wide range of tasks to validate the effectiveness of
the proposed method.

提出一种名为 RMIX 的简单实用的框架来解决超参数化神经网络中意义重要性加权方法效果不佳的问题，从而更好地应对含不同子群比例的训练和测试分布的子群转移问题，并通过混合样本上的重要性加权来获取更鲁棒的模型。同时，RMIX 还利用训练轨迹的不确定性估计来灵活地表征子群分布，并通过理论分析验证了其具有更好的广义化界限。

基于混合重赋权的子种群转移方法

Reweighted Mixup for Subpopulation Shift

Unsupervised image-to-image translation aims at learning the mapping from the
source to target domain without using paired images for training. An essential
yet restrictive assumption for unsupervised image translation is that the two
domains are aligned, e.g., for the selfie2anime task, the anime (selfie) domain
must contain only anime (selfie) face images that can be translated to some
images in the other domain. Collecting aligned domains can be laborious and
needs lots of attention. In this paper, we consider the task of image
translation between two unaligned domains, which may arise for various possible
reasons. To solve this problem, we propose to select images based on importance
reweighting and develop a method to learn the weights and perform translation
simultaneously and automatically. We compare the proposed method with
state-of-the-art image translation approaches and present qualitative and
quantitative results on different tasks with unaligned domains. Extensive
empirical evidence demonstrates the usefulness of the proposed problem
formulation and the superiority of our method.

本文针对图像转化中不对齐的情况，提出了一种基于重要性加权的图像转化方法，并与其他最新方法进行比较，展示了该方法的有效性。

学习调权来实现非对齐图像翻译

Unaligned Image-to-Image Translation by Learning to Reweight

In this paper, we study a classification problem in which sample labels are
randomly corrupted. In this scenario, there is an unobservable sample with
noise-free labels. However, before being observed, the true labels are
independently flipped with a probability $\rho\in[0,0.5)$, and the random label
noise can be class-conditional. Here, we address two fundamental problems
raised by this scenario. The first is how to best use the abundant surrogate
loss functions designed for the traditional classification problem when there
is label noise. We prove that any surrogate loss function can be used for
classification with noisy labels by using importance reweighting, with
consistency assurance that the label noise does not ultimately hinder the
search for the optimal classifier of the noise-free sample. The other is the
open problem of how to obtain the noise rate $\rho$. We show that the rate is
upper bounded by the conditional probability $P(y|x)$ of the noisy sample.
Consequently, the rate can be estimated, because the upper bound can be easily
reached in classification problems. Experimental results on synthetic and real
datasets confirm the efficiency of our methods.

本文研究了一个分类问题，其中样本标签被随机损坏。我们解决了如何在有标签噪声的情况下最好地利用传统分类问题的丰富代理损失函数，通过重要性重新加权来使用任何代理损失函数进行带有噪声标签的分类，以及如何获得噪声率的问题。