Reviews: Maximum-Entropy Fine Grained Classification

This paper presents a simple and effective approach for fine-grained image recognition. The core idea is to introduce max-entropy into loss function, because regular image classification networks often fail to distinguish semantically close visual classes in the feature space. The formulation is clear and the performance is very good in fine-grained tasks. I like the ablation study on CIFAR10/100 and different subsets of ImageNet, showing that this idea really works in classifying fine-grained concepts. The major drawback of this paper lies in its weak technical contribution.