# An Environmental Pattern Recognition Method for Traditional Chinese Settlements Using Deep Learning
## 2 Related Work
### Few-Shot Learning  
Deep learning methods have achieved remarkable success in many computer vision tasks [23; 24; 25] due to their powerful feature extraction and characterization capabilities. However, deep neural networks are prone to overfitting when the number of samples is insufficient. Although some transfer learning [36], and data augmentation [37] methods can mitigate overfitting, they do not solve it.  
Meta-learning, also called learning to learn, extracts transferable meta-knowledge from historical tasks to avoid overfitting and improve generalizability. Inspired by metric learning [38], most of the existing meta-learning image classification methods usually use the similarity of images in the feature space for classification. The idea is to learn a feature encoder that can transform an input image into a deep representation suitable for comparison. When encoded in the feature space, it is possible to compute the similarity between the support and query samples by measurement functions such as cosine similarity or Euclidean distance and compare them with each other in order to classify them in the same class. Matching Networks [39] are the first metric-based meta-learning method that maps the support set to a function via an attention model and then classifies the query samples by a weighted nearest neighbor classifier in an embedding space. Similarly, Prototypical Networks [40] create a prototype representation for each category and classify it by computing the distance between the query sample and the prototype vector. Relational Networks [41] proposes a parametric relational model to replace the ordinary distance metric, using neural networks to compute the degree of match between feature vectors. Inspired by the Prototypical Network [40], TADAM [42] extends the concept of metric space by integrating metric scaling, task conditioning, and auxiliary task co-training. MetaOptNet [43] uses discriminative linear classifiers such as support vector machines instead of nearest-neighbor classifiers (e.g., cosine similarity or Euclidean distance), using high-dimensional embeddings with improved generalization. In addition, several approaches based on pre-training have achieved competitive performance [44; 45; 46; 47]; among them, Meta-Baseline [47] provides better generalization by pre-training classifiers on all base classes and meta-learning on nearest prime-based few-sample classification algorithms. Meanwhile, [PERSON] et al. [48] proposed a graph-based few-shot learning method that converts the features extracted by a pre-trained self-supervised feature extractor into a Gaussian-like distribution to reduce feature distribution mismatch. Recently, Meta DeepBDC [49] achieved state-of-the-art performance by measuring the joint distribution between sample pairs, thus obtaining more accurate similarity.  
While meta-learning approaches have seen great success in few-shot classification, we think that fusing expert prior information and learning good embedding features is more effective than complex meta-learning algorithms in some cross-disciplinary studies. Therefore, our work prefers to improve the feature representation.