# Data Enhancement for Plant Disease Classification Using Generated Lesions
## 2 Related Work
[34] used a GAN to synthesize leaf images and improve regularization. [PERSON] et al. [35] used generated images to augment the training set and improved the accuracy of leaf-image segmentation. [PERSON] et al. [36] used spectrum normalization to improve the effect of GAN training. [PERSON] et al. [37] employed the sigmoid-adjusted straight-through estimators to estimate the gradients for the binary neurons and train the whole network by end-to-end backpropagation. [PERSON] et al. [38] used binary GANs to embed images into binary code and generate images similar to the original image. [PERSON] et al. [39] used the attention mechanism to improve the quality of wild images. [PERSON] et al. [40] used the attention mechanism to enable the network to focus on important parts of the face, improving the accuracy of facial expression recognition. [PERSON] et al. [41] used a GAN to convert RGB images to grayscale images to boost the performance for the leaf segmentation of _Arabidopsis thaliana_ in chlorophyll fluorescent imaging without any manual annotation. [PERSON] et al. [42] used the generated rose plant leaf image to expand the training set to improve the accuracy of the segmentation network. [PERSON] et al. [43] used a DCGAN to generate citrus canker images to improve the accuracy of the classification network. [PERSON] et al. [44] used fewer labels to generate a better Frechet inception distance (FID) image. [PERSON] et al. [45] used adversarial training and self-supervision to make fully unsupervised learning become scaled to attain an FID of 23.4 on unconditional ImageNet generation. [PERSON] et al. [46] applied self-supervised learning via the geometric transformation on input images and assigned the pseudo-labels to these transformed images to improve an unconditional GAN. [PERSON] et al. [47] produced images that were larger than training samples by combining them with the originally generated full image. [PERSON] et al. [48] explored how selecting a dataset affects the outcome by using three different datasets to see that a Super-Resolution GAN (SRGAN) fundamentally learns objects, and using their shape, color, and texture, redraws them in the output rather than merely attempting to sharpen edges. [PERSON] et al. [49] gradually increased the difficulty of the discriminator by progressively augmenting its input or feature space, thus enabling continuous learning of the generator, leading to a better performance. In this study, we refer to the comparison of a GAN evaluation in [PERSON] et al. [50], and we build the model of the generator network using a WGAN-GP and CiGAN as a base. Compared with the algorithm that uses the binarized image in the reference, our advantage was using the binarized image to generate a lesion image with a specific shape. It solved the problem that occurs when the random-shaped lesions are synthesized with the leaves, and so the lesions must be marked. However,due to the complexity of the lesion structures, it is difficult to mark the lesions completely using the algorithm, and manual marking needs a lot of time and money.