Adaptive image attribute editing model and editing method based on classification adversarial network

The invention provides an adaptive image attribute editing model based on a classification adversarial network, and the method achieves the accurate attribute conversion and high-quality image generation functions through the construction of an upcoiler residual network and the addition of an attribute adversarial classifier Atta-cls in a discriminator. A decoder is constructed by adopting an upper convolution residual error network Trresnet, attribute features and content features are selectively extracted, the problem of limitation of jump connection in a deep encoder decoder structure is solved, the attribute features of a target image are enhanced, a more accurate and high-quality image is generated, and the performance of a model is improved. Under the influence of the idea of the generative adversarial network, an attribute adversarial classifier Atta-cls understands the deficiency of the converted image in an adversarial learning mode for the attribute difference, and further optimizes the converted im