# Classification and Fast Few-Shot Learning of Steel Surface Defects with Randomized Network
## 2 Related Works
### Few-Shot Learning  
A learning model fed with sufficient and high-quality data is more likely to yield more-or-less accurate results. However, sometimes it is difficult to collect enough defect samples to achieve a good training model based on traditional learning structures. This can be the case in the production of steel strips, where steel surface defects are scarce and hard to collect. To overcome this problem, special approaches appeared, which can efficiently learn from a very small number of training data.  
In ref. [32] shows a simple approach for few-shot learning of steel surface defects. Three feature extraction networks (ResNet, DenseNet, and MobileNet) are compared and two kinds of feature transformations are tested (mean subtraction and L2 normalization). For one-shot learning, classification is accomplished by choosing the nearest neighbor, whereas for the multi-shot setting, the average value of the known sample feature vectors of each class is used as a class prototype to compute the nearest neighbor for the query. For the one-shot case, MobileNet reached the highest accuracy with 87.3%, while for five shots, 92.33% was achieved by DenseNet121 on the NEU dataset.  
In ref. [33], a more sophisticated semi-supervised learning model is described, which learns from both labeled and unlabeled samples. They use graph convolutional networks (GCN) [34], naming their model multiple micrographs graph convolutional network (MMGCN). GCNs construct graphs where the images are represented by nodes and their relationships by edges. Feature information propagates between connected nodes, and the distance of connected nodes is closer than the distance of the disconnected ones. MMGCN performs graph convolution by constructing multiple micrographs instead of a large graph, and labels unlabeled samples by propagating label information from labeled samples to unlabeled samples in the micrographs to obtain multiple labels, while final labels are obtained by weighting the labels. The experimental results demonstrate that the proposed MMGCN can achieve a lower computation complexity and practicality, and a higher accuracy than GCN. In fully supervised mode, MMGCN could reach 99.72% accuracy, while when the ratio of known labels was only 25%, accuracy was 98.06%.  
Another approach for label propagation is in ref. [35], where defects on the surface of lithium batteries are to be found. Training images were used to fine-tune a pre-trained ResNet10 model, then a k-NN graph was built to evaluate the distance between samples, including both the labeled and the unlabeled ones. To refine the scoring of samples, the label propagation method of ref. [36] was used. Unfortunately, while classification accuracy on their own dataset was good, the effect of label propagation was not analyzed in the article.  
Few-shot learning can be interpreted as the problem of unbalanced training sets. Ref. [37] proposes a meta-training approach to handle the unbalanced data caused by the newly arriving error classes having only few shots. Each training epoch is composed of so-called episodes and sub-sets of training data are called support sets. At the end of the episodes, the classification performance of a query set, based on knowledge gained from the respective support set, is evaluated to fine-tune the model parameters. The tuning of DNN parameters is solved by a prototypical approach. Each class is represented by a prototype feature vector and the weights of the feature extractor DNN will be tuned to separate the sample features from other prototypes the most. The superiority of this training approach is compared to other traditional classification models by evaluations on a textile dataset.