# Fine-Grained Detection of Pavement Distress Based on Integrated Data Using Digital Twin
## 2 Related Work
### Few-Shot Learning  
Few-shot learning is also called one-shot learning, which aims at achieving a knowledge transfer by assessing the similarities and differences between previous and new categories with very little data, enabling the trained model to classify new categories with a high degree of accuracy. In general, few-shot learning techniques can be classified into three categories: hallucination-based data argumentation, meta-learning, and metric-learning. Data augmentation techniques are used to generate additional training data by synthesizing new examples from existing ones. The generative-model-based augmentation method has attracted significant attention in recent times (as with a generative adversarial network (GAN) or a variational autoencoder (VAE)) to assess data distribution to generate new images that are similar in style and content to the original images. [PERSON] et al. [37] built the f-DAGAN model to generate realistic images, which showed the feasibility of synthesized data generation based on adversarial training in few-shot learning, where a few hard data can lead to sound results. [PERSON] et al. [38] proposed a new hierarchical graphical neural network (HGNN) for FSL, which can keep hierarchical correlations among nodes and extract the discriminative features of categories from individual samples. Episodic training based on the signal of node losses is utilized to update parameters, resulting in a well-generalizable model that can accurately identify new classes with limited labeled data. [PERSON] et al. [39] used focal loss instead of cross-entropy loss to help assess the sample relationship through using different penalty factors on the object and its background to optimize the model trained by imbalanced data with a few negative samples. The objective of metric learning is to learn a function that maps similar examples more closely to each other and dissimilar examples farther apart in a high-dimensional feature space. [PERSON] et al. [40] developed a deep adversarial metric learning (DAML) method for producing synthetic hard negatives from the observed negative examples, which can serve as additional training data for evaluation. [PERSON] et al. [41] employed multiple deep learning models to predict the width of cracks in a dam and analyzed the significance of influential factors in crack formation. These works indicate that the integration of deep learning models with appropriate targets is successful in learning the similarities. Meta-learning few-shot focuses on training models to quickly learn new concepts with limited training examples to lead to comprehensive understanding acquired through learning diverse assignments and generalizing acquired knowledge to new tasks. [PERSON] et al. [42] proposed a novel pavement detection based on the metric learning approach, which can lead to developing new categories from a few labeled images. The approach incorporates a new metric loss function to encourage the proximity of similar samples and separation of dissimilar samples and leverages cosine similarity between the support and test sets to infer new pavement-distress categories. [PERSON] et al. [43] proposed an embedded internal-attribute-based meta-learning classification model for structural damage identification through learning the common inter-class damage attributes, which achieves better performance compared with supervised learning.  
The above few-shot learning methods successfully use a limited quantity of preparatory information to deal with a learning model. However, the models often fall short of expectation when trained with limited shot information, so that numerous tasks for training are required. This is costly and sometimes fails because of the instability of information across different undertakings.