# Effect of Probabilistic Similarity Measure on Metric-Based Few-Shot Classification
## 2 Few-Shot Classification Problem  
The few-shot classification task is used to classify newly given samples by using an extremely limited number of training samples. The set of given training samples is called support set \(S\), and the set of new samples to be classified is called query set \(Q\). Usually, we consider the \(N\)-way \(K\)-shot problem, where the number of classes is \(N\) and the number of support samples per class is \(K\). Since the value of \(K\) is very small, it is difficult to obtain a good classification model with only support samples. Therefore, in order to develop a deep learning model for few-shot classification, it is normal to use a separate dataset that is in the same domain but has completely distinct class labels. In this approach, the main goal of the learning is to find a deep learning model that can recognize query samples from the new test classes with only a few support samples. One of the representative methods for achieving this goal is the metric-based method which tries to find an appropriate metric for classification [8; 9; 10; 12]. As shown in Figure 1, the overall structure of the metric-based method is largely composed of two modules: the embedding network and the few-shot classifier. During the learning phase, a deep network model learns to find an embedding function that maps raw inputs to feature vectors on the embedding metric space. The few-shot classifier then predicts the class of query data based on their similarity to the support data, which is measured on the metric space. The loss from the classifier is then transmitted for learning of the embedding network. Unlike the usual metric-learning problem, the few-shot learning task assumes that the classes in the test phase are unseen during the learning phase, and thus it is important to acquire a good embedding function that can generally apply to unseen test classes. To address this problem, the matching networks [8] introduced the episodic learning strategy, which is one of the meta-learning techniques. In the episodic learning strategy, subsets in the form of an \(N\)-way \(K\)-shot classification are generated by random selections from the whole training data, which are called episodes. The neural network model updates the network parameters by learning one episode at a time. By proceeding through numerous episodes, the model learns a variety of cases composed of various classes and samples. In this way, the model is not limited to the given classes but can learn more generally about the domain of the classes. That is, information about unseen classes is obtained by the use of various combinations of classes that share some common factors of the domain. Based on the episodic learning strategy, [PERSON] et al. proposed the prototypical network [9], which combines a nonlinear embedding function network and a simple distance-based classifier. Under the assumption that there exists an embedding space on which samples from each class are clustered around a single prototype, it tries to find a good embedding network through episodic learning. Once a good embedding space is found, the classification is conducted by simply finding the nearest class prototype defined as the mean of the support samples. The Euclidean distances between queries and the prototype on the embedding space are used for defining the loss function for learning of the embedding network as well, so as to create an embedding space where samples from each class are gathered near its prototype. Since the prototypical network [9] has shown better performance than more complicated few-shot learning models [6; 7; 8], there have been a number of extensions based on the same structure shown in Figure 1[10; 13; 14; 15]. [PERSON] et al. [10] added the relation module after the embedding module for more fine-grained classification. [PERSON] et al. [13] proposed to use local features as additional information to image-level features in the embedding  
Figure 1: The overall structure of the metric-based few-shot classification model. module.