In this paper, we propose a balancing training method to address problems in
imbalanced data learning. To this end, we derive a new loss used in the
balancing training phase that alleviates the influence of samples that cause an
overfitted decision boundary. The proposed loss efficiently improves the
performance of any type of imbalance learning methods. In experiments on
multiple benchmark data sets, we demonstrate the validity of our method and
reveal that the proposed loss outperforms the state-of-the-art cost-sensitive
loss methods. Furthermore, since our loss is not restricted to a specific task,
model, or training method, it can be easily used in combination with other
recent re-sampling, meta-learning, and cost-sensitive learning methods for
class-imbalance problems.

我们提出了一种平衡训练方法，通过衍生用于平衡训练阶段的新丢失，有效改善任何类型的不平衡学习方法的性能，从而解决了数据不平衡问题。我们的实验结果表明，所提出的损失优于现有的基于成本的损失方法。此外，我们的损失并不限于特定任务、模型或训练方法，因此可以轻松地与其他最近的重新采样、元学习和基于成本的学习方法结合使用以解决类别不平衡问题。

影响平衡损失函数：解决不均衡视觉分类问题

Influence-Balanced Loss for Imbalanced Visual Classification

Model learning from class imbalanced training data is a long-standing and
significant challenge for machine learning. In particular, existing deep
learning methods consider mostly either class balanced data or moderately
imbalanced data in model training, and ignore the challenge of learning from
significantly imbalanced training data. To address this problem, we formulate a
class imbalanced deep learning model based on batch-wise incremental minority
(sparsely sampled) class rectification by hard sample mining in majority
(frequently sampled) classes during model training. This model is designed to
minimise the dominant effect of majority classes by discovering sparsely
sampled boundaries of minority classes in an iterative batch-wise learning
process. To that end, we introduce a Class Rectification Loss (CRL) function
that can be deployed readily in deep network architectures. Extensive
experimental evaluations are conducted on three imbalanced person attribute
benchmark datasets (CelebA, X-Domain, DeepFashion) and one balanced object
category benchmark dataset (CIFAR-100). These experimental results demonstrate
the performance advantages and model scalability of the proposed batch-wise
incremental minority class rectification model over the existing
state-of-the-art models for addressing the problem of imbalanced data learning.

本文提出了一种基于类别不平衡的深度学习模型，使用小样本类别修正和难样本挖掘，通过迭代逐批次学习，最小化数量众多类别的影响。实验结果表明，该模型在处理不平衡数据学习问题上相比现有最先进模型具有性能优势和可扩展性。

少数类增量修正的不平衡深度学习

Imbalanced Deep Learning by Minority Class Incremental Rectification

Recognising detailed facial or clothing attributes in images of people is a
challenging task for computer vision, especially when the training data are
both in very large scale and extremely imbalanced among different attribute
classes. To address this problem, we formulate a novel scheme for batch
incremental hard sample mining of minority attribute classes from imbalanced
large scale training data. We develop an end-to-end deep learning framework
capable of avoiding the dominant effect of majority classes by discovering
sparsely sampled boundaries of minority classes. This is made possible by
introducing a Class Rectification Loss (CRL) regularising algorithm. We
demonstrate the advantages and scalability of CRL over existing
state-of-the-art attribute recognition and imbalanced data learning models on
two large scale imbalanced benchmark datasets, the CelebA facial attribute
dataset and the X-Domain clothing attribute dataset.

通过引入 Class Rectification Loss 正则化算法，利用 batch incremental hard sample mining 方案，在大规模训练数据中从少数属性类中挖掘极端样本，并发展出一种避免多数类占主导地位的端到端深度学习框架，可高效识别人脸或衣物等细节属性。试验结果显示，该框架在两个大规模标准数据集 CelebA 和 X-Domain 上，相对于现有的属性识别和不平衡数据学习模型，具有更好的优势和可扩展性。