  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.
