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With the development of deep learning, standard classification problems haveachieved good results. However, conventional classification problems are oftentoo idealistic. Most data in the natural world usually have imbalanceddistribution and fine-grained characteristics. Recently, many state-of-the-artapproaches tend to focus on one or another separately, but rarely on both. Inthis paper, we introduce a novel and adaptive batch-wise regularization basedon the proposed Adaptive Confusion Energy (ACE) to flexibly address the natureworld distribution, which usually involves fine-grained and long-tailedproperties at the same time. ACE increases the difficulty of the trainingprocess and further alleviates the overfitting problem. Through the datasetswith the technical issue in fine-grained (CUB, CAR, AIR) and long-tailed(ImageNet-LT), or comprehensive issues (CUB-LT, iNaturalist), the result showsthat the ACE is not only competitive to some state-of-the-art on performancebut also demonstrates the effectiveness of training.

ACE: Adaptive Confusion Energy for Natural World Data Distribution