FEW-SHOT LEARNING WITH REPRESENTATIVE GLOBAL PROTOTYPE

Abstract

Few-shot learning is often challenged by low generalization performance due to the assumption that the data distribution of novel classes and base classes is similar while the model is trained only on the base classes. To mitigate the above issues, we propose a few-shot learning with representative global prototype method. Specifically, to enhance the generalization to novel classes, we propose a method to jointly train the base classes and the novel classes, using selected representative and non-representative samples to optimize representative global prototypes, respectively. Additionally, a method that organically combines the sample of base classes conditional on semantic embedding to generate new samples of novel classes with the original data is proposed to enhance the data of novel classes. Results show that this training method improves the model's ability to describe novel classes, improving the classification performance for a few shots. Intensive experiments have been conducted on two popular benchmark datasets, and the experimental results show that this method significantly improves the classification ability of few-shot learning tasks and achieves state-of-the-art performance.

1. INTRODUCTION

The advantages of deep learning depend on big data, and machines can learn effectively under the drive of big data. However, an insufficient amount of data leads to problems, such as model overfitting, which makes the model not fit well on the dataset outside the training data, showing weak generalization ability Kang et al. (2019) . Thus, few-shot learning for small datasets has become a key technology to solve this type of problem. Few-shot learning (FSL) refers to the use of a small amount of labeled data per class training to make the model show higher performance, in line with the law of human learning. Meta-learning is the basis of most of the existing FSL methods. In meta-learning, training is modeled after a test, i.e., both base and novel classes learn a task consisting of a few samples from N-way K-shot, after which knowledge is transferred from the base classes in the form of appropriate initial conditions Finn et al. ( 2017 2017). There is, however, a fundamental limitation to all of these approaches: the models assumed that the distribution of base class data and novel class data was similar, so they mostly learned using only base class data, which does not guarantee that they are generalizable to novel class data. A few-shot learning method uses the transferability of knowledge to learn the ability to characterize samples from novel classes with only a few markers, while traditional machine learning and deep learning feed all classes of samples to the model at the beginning of training. Once the data distribution has low consistency, transferred knowledge is not applicable to generalize the novel class of data. Thus, learning the global representation of all class data together can alleviate the problem of model overfitting on the base classes like machine learning and deep learning. The novel class data is learned at the beginning, and the metric novel class data and the global representation strengthen the discriminative nature of the novel class samples Wang et al. (2018); Li et al. (2019) . At the same time, the sample synthesis strategy of Wang et al. ( 2018) is used to enhance the samples of the novel classes to alleviate the sample imbalance. However, since the global prototype is optimized directly using all samples, it is not representative. Moreover, the sample generation method actually requires



); Fei et al. (2020); Zhang et al. (2020), embeddings Vinyals et al. (2016); Chopra et al. (2005); Hadsell et al. (2006); Sung et al. (2018) or optimization strategies Ravi & Larochelle (

