Multi-label classification models have a wide range of applications in E-commerce, including visual-based label predictions and language-based sentiment classifications. A major challenge in achieving satisfactory performance for these tasks in the real world is the notable imbalance in data distribution. For instance, in fashion attribute detection, there may be only six 'puff sleeve' clothes among 1000 products in most E-commerce fashion catalogs. To address this issue, we explore more data-efficient model training techniques rather than acquiring a huge amount of annotations to collect sufficient samples, which is neither economic nor scalable. In this paper, we propose a state-of-the-art weighted objective function to boost the performance of deep neural networks (DNNs) for multi-label classification with long-tailed data distribution. Our experiments involve image-based attribute classification of fashion apparels, and the results demonstrate favorable performance for the new weighting method compared to non-weighted and inverse-frequency-based weighting mechanisms. We further evaluate the robustness of the new weighting mechanism using two popular fashion attribute types in today's fashion industry: sleevetype and archetype.

翻译：多标签分类模型在电子商务中具有广泛应用，包括基于视觉的标签预测和基于语言的情感分类。在实际应用中实现这些任务的理想性能面临的一个主要挑战是数据分布的显著不平衡。例如，在时尚属性检测中，大多数电商时尚目录中每1000件商品可能仅有六件“泡泡袖”服装。为解决该问题，我们探索了更具数据效率的模型训练技术，而非通过获取大量标注来收集充足样本——这既不符合经济性也无法扩展。本文提出了一种先进的加权目标函数，用于提升深度神经网络在长尾数据分布下进行多标签分类的性能。我们的实验基于时尚服装的图像属性分类，结果表明，与无权重机制及基于逆频率的加权机制相比，新提出的加权方法表现优异。我们进一步使用当今时尚领域两种主流属性类型——袖型（sleevetype）与原版型（archetype），评估了新加权机制的鲁棒性。