# Skeleton-Based Dynamic Hand Gesture Recognition Using an Enhanced Network with One-Shot Learning
## 2 Related Work
### One-Shot Learning  
The implementations of one-shot learning can be divided into statistics-based, weight-based matching, and meta-learning. For the statistics-based, [PERSON] [37] adopted the Bayesian framework realized one-shot learning of handwritten character pictures based on the statistical point of view and the way humans learn things, triggering the new wave of one-shot learning.  
Besides the above statistics, there are also many methods on the basis of weighted matching for one-shot learning, which performs certain criteria modeling on known samples and then determines the class according to the distance of samples. The most typical method is the k-nearest neighbor (KNN), which is a nonparametric estimation method that can directly employ distance to determine the category without prior training. Another method is to learn an end-to-end nearest neighbor classifier, which can not only quickly learn new samples but also have a great generalization of known samples. [PERSON] et al. [38] carried out classification by calculating the distance from prototype representations of each class, which turns into the nearest neighbor classification in the metric space. While [PERSON] et al. [39] performed efficacious feature extraction on new samples by limiting input methods, then used supervised metric learning based on twin networks to train and finally reused features extracted by that network for small or no sample learning. Similarly, [PERSON] et al. [40] also utilized metric learning based on deep neuro features, which uses external memory to enhance the neural network that maps a small labeled support set and an unlabeled example to its label, obviating the need for fine-tuning to adapt to new class types.  
Meta-learning, also known as \"learning to learn\", aims to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples [41]. A neural network with memory can implement meta-learning, but its memory storage is limited. A large number of new features may exceed the memory storage capacity so that the network cannot learn new tasks. The NTM network can solve this problem, as it is capable of both long-term storage via slow updates of its weights and short-term storage via its external memory module [16]. Based on the NTM network, [PERSON] et al. [9] introduced a memory access module that emphasizes accurate encoding of relevant (recent) information and pure content-based retrieval to implement meta-learning. Besides, [PERSON] et al. [42] proposed an LSTM-based meta-learner model, whose parameterization allows it to learn appropriate parameter updates specifically for the scenario where a set amount of updates will be made, while also learning a general initialization of another learner (classifier) network that allows for quick convergence of training.  
In general, the current one-shot learning-based methods are in a booming period. However, there is still no appropriate method for one-shot learning with skeleton-based hand gesture recognition. Therefore, this paper will study the current advanced achievements and propose a suitable algorithm to realize hand gesture recognition in line with one-shot learning.