Real-world data often follow a long-tailed distribution as the frequency of
each class is typically different. For example, a dataset can have a large
number of under-represented classes and a few classes with more than sufficient
data. However, a model to represent the dataset is usually expected to have
reasonably homogeneous performances across classes. Introducing class-balanced
loss and advanced methods on data re-sampling and augmentation are among the
best practices to alleviate the data imbalance problem. However, the other part
of the problem about the under-represented classes will have to rely on
additional knowledge to recover the missing information.
In this work, we present a novel approach to address the long-tailed problem
by augmenting the under-represented classes in the feature space with the
features learned from the classes with ample samples. In particular, we
decompose the features of each class into a class-generic component and a
class-specific component using class activation maps. Novel samples of
under-represented classes are then generated on the fly during training stages
by fusing the class-specific features from the under-represented classes with
the class-generic features from confusing classes. Our results on different
datasets such as iNaturalist, ImageNet-LT, Places-LT and a long-tailed version
of CIFAR have shown the state of the art performances.

本文提出了一种新方法，通过在特征空间中使用来自具有丰富样本的类别所学习的特征来增强特征空间中的欠表示类别，从而解决长尾分布问题。通过使用类别激活映射，将每个类别的特征分解为类别通用和类别特定部分，并将欠表示类别的类别特定特征与混淆类别的类别通用特征融合，实时生成新样本。结果表明，在 iNaturalist、ImageNet-LT、Places-LT 以及 CIFAR 的长尾版本等数据集上，本文所提出的方法展现了最先进的表现水平。

长尾数据的特征空间增强

Feature Space Augmentation for Long-Tailed Data

With the rapid increase of large-scale, real-world datasets, it becomes
critical to address the problem of long-tailed data distribution (i.e., a few
classes account for most of the data, while most classes are
under-represented). Existing solutions typically adopt class re-balancing
strategies such as re-sampling and re-weighting based on the number of
observations for each class. In this work, we argue that as the number of
samples increases, the additional benefit of a newly added data point will
diminish. We introduce a novel theoretical framework to measure data overlap by
associating with each sample a small neighboring region rather than a single
point. The effective number of samples is defined as the volume of samples and
can be calculated by a simple formula $(1-\beta^{n})/(1-\beta)$, where $n$ is
the number of samples and $\beta \in [0,1)$ is a hyperparameter. We design a
re-weighting scheme that uses the effective number of samples for each class to
re-balance the loss, thereby yielding a class-balanced loss. Comprehensive
experiments are conducted on artificially induced long-tailed CIFAR datasets
and large-scale datasets including ImageNet and iNaturalist. Our results show
that when trained with the proposed class-balanced loss, the network is able to
achieve significant performance gains on long-tailed datasets.

研究长尾数据在机器学习任务中的问题，并提出了使用 “有效样本数” 改进重新权重计算的类平衡损失方法来提高对长尾数据集的训练效果。在人工合成的长尾 CIFAR 数据集和大规模数据集 ImageNet 和 iNaturalist 上进行了全面的实验，并证明该方法能够显著提高长尾数据集上的网络表现。