Abstract
Abstract Sea objects training, the conditional adversarial networks require a large number of images to solve image-to-image translation problems. In the case of insufficient samples, it leads to network overfitting and poor training results. This project proposes a conditional adversarial generative model that retains the original background features in the absence of paired samples. The goal of this project is to reduce the deviation of the corresponding output from the original input. Firstly, the object images of different categories are labeled with color masks. Second, sea objects are generated randomly in the original background using model of this project. Finally, the generated results of this approach are compared with other approaches. The experimental results show that, compared with results from other conditional adversarial generative models, the generated object images using model of this project have the characteristics of richer texture and clearer structure.
Highlights
Image generation is one of the popular research domain of computer vision
For machine learning, it can train a large number of images using Generative Adversarial Network (GAN) so as to generate new images
The sea object image is generated by adding conditional mask in the specified position under the condition of less input of original images
Summary
Image generation is one of the popular research domain of computer vision. It is a technology for generating images based on known content (e.g. text, image). For machine learning, it can train a large number of images using Generative Adversarial Network (GAN) so as to generate new images. GAN generates samples from random noise, so it exists the defect of uncontrollable information generation and free training process. CGAN2 adds additional conditional information to GAN in order to control the training process of the generator and the discriminator. The existing data-driven CGAN has far less generality than human learning ability. In the absence of data, it has practical significance that how to imitate the human learning process and design a more reasonable method to generate image
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