## 1 Introduction
### Related Works
#### 1.2.2 Meta-Learning  
To address the constraint inherent in traditional machine learning, which demands an extensive array of labeled signal samples, we introduce the concept of meta-learning. Meta-learning, a learning paradigm inspired by biological systems, strives to transcend the limitations of conventional deep learning networks regarding their capacity for generalization and adaptability across diverse scenarios. The core idea involves improving learning algorithms or models by discerning relationships among interconnected tasks, mitigating challenges posed by limited data and subpar generalization in traditional deep learning. In typical machine learning, models are trained with abundant labeled data to learn associations between inputs and outputs. In contrast, meta-learning entails a training phase where models accumulate experience from diverse tasks. In the subsequent testing phase, models rapidly adapt to new tasks with limited labeled samples.  
Tasks in meta-learning consist of a support set and a query set:  
* The support set, a small example collection, trains the model on the same classes it will be tested on. The model derives insights from this set to update its parameters and apply them to the query set.
* The query set, used for evaluation, tests the model using knowledge acquired from the support set. It guides the model's training process.  
Furthermore, meta-learning often employs the N-way K-shot task setting. In each task, the model learns from N categories, each containing K samples in the support set.  
Within the realm of deep learning tasks, meta-learning methods can be implemented through two primary approaches: metric-based and optimization-based. In metric-based meta-learning, the objective is to acquire an appropriate metric space for classification by comparing the distances between sample features. [PERSON] et al. [2018] proposed matching networks for one-shot learning. It utilizes an attention mechanism and matching process to classify query samples based on support set samples. [PERSON] et al. [2018] presented prototypical networks for few-shot learning. It learns prototype vectors for each class in the feature space and performs classification based on the distance between query samples and prototype vectors. [PERSON] et al. [2018] introduced relation networks for few-shot learning. It learns the relationship between query samples and support set samples through a sub-network and uses the learned representation to classify query samples. Conversely, optimization-based meta-learning aims to learn superior network initialization parameters by drawing insights from related tasks. Encountering new tasks, the network is fine-tuned with a small number of samples to achieve performance comparable to conventional deep learning models trained on extensive data. [PERSON] et al. [2018] proposed Model-Agnostic Meta-Learning (MAML) for fast adaptation of deep networks. It optimizes the initialization parameters through repeated adjustments on different tasks, enabling quick adaptation to new tasks. Reptile [2018] is an iterative meta-learning algorithm that approximates the gradient descent process. It updates the model parameters multiple times on each task and performs a weighted average with the initial parameters to learn a model with good generalization. [PERSON] et al. [2018] introduced LEO (Latent Embedding Optimization) as a meta-learning method. It learns a low-dimensional representation space where samples are mapped. It employs gradient descent to train the embedding network, enabling fast adaptation to new tasks using a few samples.