Multi-Attribute Learning With Highly Imbalanced Data

Viviana Beltrán, Mickaël Coustaty, Nicholas Journet, Juan C. Caicedo, Antoine Doucet

Abstract: Data is one of the most important keys for success when studying a simple or a complex phenomenon. With the use of deep learning exploding and its democratization, non-computer science experts may struggle to use highly complex deep learning architectures, even when straightforward models offer them suitable performances. In this article, we study the specific and common problem of data imbalance in real databases as most of the bad performance problems are due to the data itself. We cover two keys aspects. First, we propose ways to deal with the situation when a data set contains different levels of imbalance. Classical imbalanced learning strategies cannot be directly applied when using multi-attribute deep learning models, i.e., multi-task or multi-label architectures. Therefore, one of our contributions is a proposed adaptation to face each one of the problems derived from imbalance. Second, we demonstrate that with little to no imbalance, straightforward deep learning models work well. However, for non-experts, these models can be seen as black boxes, where all efforts are invested in preprocessing the data. To simplify the problem, we perform the classification task without features that are costly to extract, such as part localization which is widely used in the state of the art of attribute classification. We make use of three widely known attribute databases, CUB-200-2011 - CUB as our main use case due to its deeply imbalanced nature, along with two balanced databases: celebA and AwA2. All of them contain multi-attribute annotations. The results of very fine-grained attribute learning demonstrate that in the presence of imbalance, our proposed strategies make it possible to have competitive results against the state of the art while taking advantage of multi-attribute deep learning models. We also noticed an increase in performance while using a specialized loss function (Focal Loss). For CUB, we have competitive results, and for CelebA and AwA2 our strategies over-perform the state of the art.