A number of computer vision deep regression approaches report improved
results when adding a classification loss to the regression loss. Here, we
explore why this is useful in practice and when it is beneficial. To do so, we
start from precisely controlled dataset variations and data samplings and find
that the effect of adding a classification loss is the most pronounced for
regression with imbalanced data. We explain these empirical findings by
formalizing the relation between the balanced and imbalanced regression losses.
Finally, we show that our findings hold on two real imbalanced image datasets
for depth estimation (NYUD2-DIR), and age estimation (IMDB-WIKI-DIR), and on
the problem of imbalanced video progress prediction (Breakfast). Our main
takeaway is: for a regression task, if the data sampling is imbalanced, then
add a classification loss.

在计算机视觉的深度回归方法中，添加一个分类损失到回归损失中可以得到改进的结果，尤其对于存在不平衡数据的回归任务尤为明显，因此我们建议在不平衡数据的回归任务中添加一个分类损失。

理解分类如何有助于回归的方法

A step towards understanding why classification helps regression

Deep regression is an important problem with numerous applications. These
range from computer vision tasks such as age estimation from photographs, to
medical tasks such as ejection fraction estimation from echocardiograms for
disease tracking. Semi-supervised approaches for deep regression are notably
under-explored compared to classification and segmentation tasks, however.
Unlike classification tasks, which rely on thresholding functions for
generating class pseudo-labels, regression tasks use real number target
predictions directly as pseudo-labels, making them more sensitive to prediction
quality. In this work, we propose a novel approach to semi-supervised
regression, namely Uncertainty-Consistent Variational Model Ensembling (UCVME),
which improves training by generating high-quality pseudo-labels and
uncertainty estimates for heteroscedastic regression. Given that aleatoric
uncertainty is only dependent on input data by definition and should be equal
for the same inputs, we present a novel uncertainty consistency loss for
co-trained models. Our consistency loss significantly improves uncertainty
estimates and allows higher quality pseudo-labels to be assigned greater
importance under heteroscedastic regression. Furthermore, we introduce a novel
variational model ensembling approach to reduce prediction noise and generate
more robust pseudo-labels. We analytically show our method generates higher
quality targets for unlabeled data and further improves training. Experiments
show that our method outperforms state-of-the-art alternatives on different
tasks and can be competitive with supervised methods that use full labels. Our
code is available at this https URL

本文提出了一种新的半监督回归方法，即不确定性一致变分模型集成（UCVME），通过生成高质量的伪标签和不确定性估计来改善训练，并且引入一种新的变分模型集成方法来减少预测噪声和生成更稳健的伪标签。实验结果表明，我们的方法在不同任务上优于现有的替代方法，并且可以与使用完全标签的监督方法竞争。

基于贝叶斯神经网络的不确定性一致性半监督深度回归和变分模型集成

Semi-Supervised Deep Regression with Uncertainty Consistency and  Variational Model Ensembling via Bayesian Neural Networks

In this paper, we present a deep regression approach for face alignment. The
deep architecture consists of a global layer and multi-stage local layers. We
apply the back-propagation algorithm with the dropout strategy to jointly
optimize the regression parameters. We show that the resulting deep regressor
gradually and evenly approaches the true facial landmarks stage by stage,
avoiding the tendency to yield over-strong early stage regressors while
over-weak later stage regressors. Experimental results show that our approach
achieves the state-of-the-art

本文介绍一种深度回归方法，包括全局层和多阶段局部层。采用反向传播算法和 Dropout 策略进行联合优化。实验证明，该方法逐步且平均地逼近真实的人脸关键点，避免了早期回归器过强和后期回归器过弱的情况，最终取得了最先进的效果。