Real-world data is often unbalanced and long-tailed, but deep models struggle
to recognize rare classes in the presence of frequent classes. To address
unbalanced data, most studies try balancing the data, the loss, or the
classifier to reduce classification bias towards head classes. Far less
attention has been given to the latent representations learned with unbalanced
data. We show that the feature extractor part of deep networks suffers greatly
from this bias. We propose a new loss based on robustness theory, which
encourages the model to learn high-quality representations for both head and
tail classes. While the general form of the robustness loss may be hard to
compute, we further derive an easy-to-compute upper bound that can be minimized
efficiently. This procedure reduces representation bias towards head classes in
the feature space and achieves new SOTA results on CIFAR100-LT, ImageNet-LT,
and iNaturalist long-tail benchmarks. We find that training with robustness
increases recognition accuracy of tail classes while largely maintaining the
accuracy of head classes. The new robustness loss can be combined with various
classifier balancing techniques and can be applied to representations at
several layers of the deep model.

本文提出了一种基于鲁棒性理论的新型损失函数，旨在解决深度模型在处理不平衡数据时的分类偏差问题，从而提高对于长尾类别的识别准确性。在多项基准测试中，通过降低特征空间中头类别的表示偏差，该方法相较于已有方法以及 SOTA 方法均得到了更好的效果。

长尾学习的分布鲁棒性损失

Distributional Robustness Loss for Long-tail Learning

Many applications of unpaired image-to-image translation require the input
contents to be preserved semantically during translations. Unaware of the
inherently unmatched semantics distributions between source and target domains,
existing distribution matching methods (i.e., GAN-based) can give undesired
solutions. In particular, although producing visually reasonable outputs, the
learned models usually flip the semantics of the inputs. To tackle this without
using extra supervision, we propose to enforce the translated outputs to be
semantically invariant w.r.t. small perceptual variations of the inputs, a
property we call "semantic robustness". By optimizing a robustness loss w.r.t.
multi-scale feature space perturbations of the inputs, our method effectively
reduces semantics flipping and produces translations that outperform existing
methods both quantitatively and qualitatively.

针对许多不成对图像翻译应用需要在翻译过程中保持输入内容的语义，而现有的分布匹配方法容易导致翻译出现意义的混淆，本文提出了一种基于对抗学习的方法来实现对输入内容的意义保护，称为 “语义鲁棒性”，该方法使用多尺度特征空间扰动对鲁棒性损失进行优化，能够有效降低输出的语义翻转现象，并在定量和定性方面优于现有方法。