Image generation is a hot topic in the academic recently,and has been applied to AI drawing,which can bring Vivid AI paintings without labor costs.In image generation,we represent the image as a random vector,assuming...Image generation is a hot topic in the academic recently,and has been applied to AI drawing,which can bring Vivid AI paintings without labor costs.In image generation,we represent the image as a random vector,assuming that the images of the natural scene obey an unknown distribution,we hope to estimate its distribution through some observation samples.Especially,with the development of GAN(Generative Adversarial Network),The generator and discriminator improve the model capability through adversarial,the quality of the generated image is also increasing.The image quality generated by the existing GAN based image generation model is so well-paint that it can be passed for genuine one.Based on the brief introduction of the concept ofGAN,this paper analyzes themain ideas of image synthesis,studies the representative SOTA GAN based Image synthesis method.展开更多
文摘Image generation is a hot topic in the academic recently,and has been applied to AI drawing,which can bring Vivid AI paintings without labor costs.In image generation,we represent the image as a random vector,assuming that the images of the natural scene obey an unknown distribution,we hope to estimate its distribution through some observation samples.Especially,with the development of GAN(Generative Adversarial Network),The generator and discriminator improve the model capability through adversarial,the quality of the generated image is also increasing.The image quality generated by the existing GAN based image generation model is so well-paint that it can be passed for genuine one.Based on the brief introduction of the concept ofGAN,this paper analyzes themain ideas of image synthesis,studies the representative SOTA GAN based Image synthesis method.
