# A Large-Class Few-Shot Learning Method Based on High-Dimensional Features
## 2 Related Work
[8] put forward a class hierarchy that uses the semantic relationships between classes to guide the network to learn more transferable feature information, thereby helping the nearest neighbor algorithm to obtain more accurate classification results. Ref. [9] proposed a multimodal knowledge-discovery method. The foreground image and background image of a sample are obtained through saliency detection. The original image, foreground image, and background image are respectively input into the corresponding network to extract features. The three feature maps obtained are spliced into a visual feature, and the semantic weak supervision information of the class is used. Ref. [7] introduced a confusion matrix to analyze the easily confused categories in a dataset, so as to improve the recognition accuracy of neural networks for large-class few-shot learning. These methods can improve recognition accuracy in large-class few-shot learning tasks to a certain extent; however, there is still much room for improvement in practical applications. Continuous learning: Continuous learning [27] refers to the ability of a system to continuously learn and adapt when processing new data without forgetting what it has learned. A representative scenario is few-shot continual learning (FSCL). FSCL requires CNN models to incrementally learn new classes from very few labelled samples, without forgetting the previously learned ones. Ref. [28] represents previously learned knowledge using a neural gas (NG) network, which can learn and preserve the topology of the feature manifold formed by different classes. The model first learns some base classes for initialization with a large number of training samples, and, then, learns a sequence of novel classes with only a small number of training samples. Ref. [29] introduces a data-free replay method based on entropy regularization to solve the FSCL problem. It proves that the data-replay method is effective in solving the FSCL problem, and it proposes a data-independent replay method to avoid privacy issues. High-dimensional feature space learning: Recently, some scholars have conducted FSL theoretical research based on a high-dimensional feature space [11; 12; 13; 14; 15]. Ref. [11] proposed that, with certain conditions, the precision of few-shot classification can improve as the number of classes grows. Ref. [15] presents theoretical research on high-dimensional space, and the authors prove that, as long as samples from the same class can be distributed compactly in the high-dimensional space and samples from different classes can be fully dispersed, the model of large-class FSL can achieve better generalization. This theoretical research give us some proof that the large-class FSL problem could be solved in high-dimensional space. First, we normalized the feature embedding to unit length, projected the samples onto the unit sphere, and then used the global orthogonal regularization strategy to make the samples from different categories as orthogonal as possible on the hypersphere, so as to achieve the goal of compactness within the class and the dispersion between classes in high-dimensional space. The classification loss function: softmax loss is the most commonly used loss function. It maps the flattened output of the feature map to \((0,1)\) and outputs the probability of each class. Many variants of the softmax loss function have been proposed to maximize the distance between classes and reduce the distance within a class. Ref. [30] proposed center loss, which is used to constrain the intraclass compactness of features. With the development of face recognition technology, A-Softmax Loss [31], CosFace Loss [32], AM-Softmax Loss [33], and other loss functions based on the angular distance metric in high-dimensional feature space have been proposed, and have achieved remarkable classification performances. Since HF-FSL distributes the samples on the hypersphere, it is more appropriate to use the loss function based on the angular distance metric; hence, AM-Softmax Loss was adopted in this study.