Title: Effectively Clarify Confusion via Visualized Aggregation and Separation of Deep Representation. Abstract: Clarifying confusion is the most critical issue for improving classification performance. Confusion occurs with almost all classification models but tends to be ignored in excellent-performing models. The current mainstream research mainly focuses on solving the confusion in a specific case, such as data insufficiency and class imbalance. We believe that mining the commonalities of the same class and the gaps among different classes will effectively clarify the confusion. In this paper, we propose a novel, simple and intuitive Aggregation Separation Loss (ASLoss), as an adjunct for classification loss to clarify the confusion in some common cases. The ASLoss aggregates the representations of the same class samples as near as possible and separates the representations of different classes as far as possible.
We use two image classification tasks with three simultaneous confounding characteristics i.e. data insufficiency, class imbalance, and unclear class evidence to demonstrate ASLoss. Representation visualization, confusion comparison and detailed comparison experiments are conducted. The results show that representations in deep spaces extracted by ASLoss are sufficiently clear and distinguishable, the confusion among different classes is significantly clarified and the optimal network using ASLoss reaches the state-of-the-art level.