  With the development of deep learning, standard classification problems have
achieved good results. However, conventional classification problems are often
too idealistic. Most data in the natural world usually have imbalanced
distribution and fine-grained characteristics. Recently, many state-of-the-art
approaches tend to focus on one or another separately, but rarely on both. In
this paper, we introduce a novel and adaptive batch-wise regularization based
on the proposed Adaptive Confusion Energy (ACE) to flexibly address the nature
world distribution, which usually involves fine-grained and long-tailed
properties at the same time. ACE increases the difficulty of the training
process and further alleviates the overfitting problem. Through the datasets
with the technical issue in fine-grained (CUB, CAR, AIR) and long-tailed
(ImageNet-LT), or comprehensive issues (CUB-LT, iNaturalist), the result shows
that the ACE is not only competitive to some state-of-the-art on performance
but also demonstrates the effectiveness of training.
