# Review of Image Augmentation Used in Deep Learning-Based Material Microscopic Image Segmentation
## 2 Method Review
### Deep Learning-Based Image Augmentation Methods
#### 2.2.1 Autoaugment  
The combination of various basic data augmentation methods could potentially enhance the accuracy of deep neural networks. However, the performance gains from the combination of individual basic data augmentation methods are not linearly additive. Thus, manually combining basic augmentation methods could be time-consuming and ineffective. AutoAugment [39] addresses this issue by defining a search space that contains multiple \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Method** & **Description** \\ \hline \multirow{8}{*}{AutoAugment} & AutoAugment & Searches the optimal data augmentation methods for origin data automatically by using a search algorithm. \\ \cline{2-3}  & Fast-AutoAugment & Uses a more effective density matching-based search algorithm. \\ \cline{2-3}  & Faster-AutoAugment & Employs a differentiable search algorithm to further improve the efficiency of automatic search. \\ \cline{2-3}  & RandAugment & Expands the search space, speeds up the ideal augmentation \\ \cline{2-3}  & Greedy AutoAugment & Employs a greedy-based search algorithm. \\ \cline{2-3}  & UniformAugment & Avoids a significant number of unwanted searches. \\ \cline{2-3}  & KeepAugment & Detects important regions in the original image and retains this information via saliency map. \\ \cline{2-3}  & Deep & \multirow{2}{*}{Learns an augmentation strategy, even in the absence of default augmentation.} \\ \cline{2-3}  & AutoAugmentation & & \\ \hline \multirow{8}{*}{GAN} & DCGAN & Led to the explosion of realistic image generation. \\ \cline{2-3}  & Pix2 Pix & Uses aligned input images to learn the mapping relationship between real and synthetic images. \\ \cline{2-3}  & CycleGAN & Enables training with unpaired samples. \\ \cline{2-3}  & Pix2 PixHD & Output can be manually reconstructed, semantically segmented, labeled images. \\ \cline{2-3}  & two-stage GAN & Collaborates creation of paired images and masks. \\ \cline{2-3}  & task-guided GAN & A task-guided branching was integrated into the end of the generator. \\ \cline{2-3}  & GANSeg & Generates images conditional on potential masks. \\ \cline{2-3}  & transfer-based GAN & Creates a bridge for simulated data-assisted predictive analysis of real data. \\ \hline \multirow{2}{*}{Learned transformations method} & Captures image transformations such as nonlinear deformation and imaging intensity. \\ \cline{2-3}  & & \\ \end{tabular}
\end{table} 
Table 3: Classification overview of deep learning-based image augmentation methods. basic data augmentation algorithms. The approach searches the optimal data augmentation methods for origin data automatically by using a reinforcement learning-based search algorithm. It has achieved the highest accuracy on various image classification data sets, including CIFAR-10 [40] and ImageNet [41], and has been experimentally proven to be more transferable. Nevertheless, AutoAugment's search algorithm requires 5000 h of GPU training time to find the most suitable strategy on the CIFAR-10 data set with 4000 images due to some time-consuming steps in the search process. To tackle this bottleneck problem, [PERSON] et al. [42] proposed Fast-AutoAugment, which uses a more effective density matching-based search algorithm to accelerate the process of searching the optimal strategy combination. With this improvement, the processing time on the CIFAR-10 data set was reduced to 3.5 GPU h. In 2020, [PERSON] et al. [43] proposed Faster-AutoAugment, which employs a differentiable search algorithm to further improve the efficiency of automatic search. In addition to optimizing the search strategy, the AutoAugment team then proposed RandAugment [44], which described that expanding the search space can help speed up the ideal augmentation combination approach. As there exists a significant inverse relationship between data augmentation strategy and data size, [PERSON] et al.