Tire Pattern Classification Based On Few-Shot Learning

Learning from a limited number of samples is challenging since the learned model can easily become overfitted based on the biased distribution formed by only a few training examples. In this paper, we design a novel few-shot learning method to solve the problem of tire pattern classification proposed by Xi’an University of Posts and Telecommunications laboratory. The proposed method consists of two steps. On the one hand, we calibrate the distribution of these fewsample classes by transferring statistics from the classes with sufficient features (FD). On the other hand, an adequate number of examples can be sampled from the feature distribution to expand the inputs to the classifier (SVM). Experimental results on the Tire pattern dataset demonstrate that, compared with the existing few-shot learning models, the proposed FT with SVM provides noticeably more robust and higher performance, thus making it a useful tool for practical application scenarios.