Few-shot fine-grained image classification aims to solve the learning problem with few limited labeled examples. The existing methods use data augmentation to randomly transform the original examples to get new examples. and then use the new examples to train the model to improve the robustness and generalization ability of the learnt model. Due to each iteration of these methods uses... https://www.roneverhart.com/Infraction/