关键词: 生成对抗网络, 信息最大化模型, 条件模型, 深度卷积网络, 图像识别

Abstract:

Traditional image recognition methods require a large number of labeled samples for training, and training model is difficult to achieve stability. Aiming at these problems, a Conditional Information Deep Convolution Generative Adversarial Network（C-Info-DCGAN） is established by combining the structural advantages of the conditional generation network and the information maximization generative adversarial network. The model adds the category information and potential information of the image as input data, and then uses the Q network to better play the guiding role of the category information and potential information on training, and uses a deep convolution network to enhance the ability to extract image features. Experimental results show that this method can accelerate the model training convergence speed and effectively improve the accuracy of image recognition.

Key words: generative adversarial network, information maximization model, conditional model, deep convolution network, image recognition