Title: Natural World Distribution via Adaptive Confusion Energy Regularization. Abstract: We introduce a novel and adaptive batch-wise regularization based on the proposed Batch Confusion Norm (BCN) to flexibly address the natural world distribution which usually involves fine-grained and long-tailed properties at the same time. The Fine-Grained Visual Classification (FGVC) problem is notably characterized by two intriguing properties, significant inter-class similarity and intra-class variations, which cause learning an effective FGVC classifier a challenging task. Existing techniques attempt to capture the discriminative parts by their modified attention mechanism. The long-tailed distribution of visual classification poses a great challenge for handling the class imbalance problem. Most of existing solutions usually focus on the class-balancing strategies, classifier normalization, or alleviating the negative gradient of tailed categories. Depart from the conventional approaches, we propose to tackle both problems simultaneously with the adaptive confusion concept. When inter-class similarity prevails in a batch, the BCN term can alleviate possible overfitting due to exploring image features of fine details. On the other hand, when inter-class similarity is not an issue, the class predictions from different samples would unavoidably yield a substantial BCN loss, and prompt the network learning to further reduce the cross-entropy loss. More importantly, extending the existing confusion energy-based framework to account for long-tailed scenario, BCN can learn to exert proper distribution of confusion strength over tailed and head categories to improve classification performance. While the resulting FGVC model by the BCN technique is effective, the performance can be consistently boosted by incorporating extra attention mechanism. In our experiments, we have obtained state-of-the-art results on several benchmark FGVC datasets, and also demonstrated that our approach is competitive on the popular natural world distribution dataset, iNaturalist2018. 