# Local Contrast Learning for One-Shot Learning
## 1 Introduction  
Currently, deep learning is always tightly related to big data. It has become a unanimous agreement that the success of deep learning is the result of the big labeled datasets [1, 2]. Deep learning provides a kind of end-to-end approach for object recognition that is more generalizable and robust than the traditional methods based on feature extraction, but it is difficult to implement because of its need for large annotated datasets [3]. Building a large training set for deep learning is sometimes so extremely expensive and not acceptable, which hinders the construction of more practical applications. The machine learning methods of learning from one sample or few samples (which are usually called one-shot learning or few-shot learning [4]; in order to simplify, we call them one-shot learning) attract broad attention. One-shot learning means learning a novel concept from one sample or only a few samples. That is still an open challenge for object recognition due to a lack of training samples to tune the parameters of a learning model, which might result in a serious overfitting problem. Some learning techniques based on different theories have been proposed for tackling the problem based on different theories, such as transfer learning [5], meta-learning [6], metric learning [7], and data augmentation-based learning [8, 9]; however, it is still ongoing. Many scholars have conducted relevant research in this field. For instance, [PERSON] et al. [10] proposed the Neural Network Augmented with a Task-Adaptive Projection method, which utilizes a scenario-based meta-learning strategy and linear projection for image classification. [PERSON] et al. [11] extracted features from different layers basedon coarse/fine-grained relation networks, where coarse-grained classification was applied to shallow features followed by fine-grained classification on deep features. Although few-shot learning enables learning from a limited number of labeled data and recognizing unseen data, it still requires a relatively large number of training data categories during the training process. This still poses a significant challenge in specific specialized domains like medicine [12] and industrial fields [13], where obtaining or creating numerous data categories is frequently arduous and expensive. For instance, in the case of certain rare diseases, the availability of sample data may be extremely limited, posing significant challenges in constructing datasets of substantial scale [14]. In scenarios with limited training sample categories, samples typically occupy only a tiny portion of the high-dimensional space, presenting a significant sparsity issue. However, by treating a sample category as a \"reference point\" and comparing other category samples against it, the challenge of sample sparsity can be effectively overcome. Within the local space formed around a sample, serving as a reference point, the distribution of samples is relatively dense compared to that in the global space, thereby alleviating the sparsity issue present in the global space. By comparing the relationships between samples within the local space, particularly by observing other category samples relative to a category sample used as a reference point, requiring only a few features for discrimination, sample classification and recognition can be facilitated. Taking Figure 1 as an example (all facial images in the figure are generated by AI), individuals of the same race form a local space, while individuals of all races form a global space. When Asians observe other Asians, they can easily distinguish each other. However, when Asians attempt to differentiate Africans or Americans, they encounter greater challenges. This is due to the fact that, in the cognitive domain of human perception, there is greater familiarity and understanding of the facial features and skin tones of unfamiliar but same-race individuals.