Abstract:

Generative adversarial networks (GAN) are widely used in image generation. However, there is still a big gap between the samples generated by unsupervised and supervised networks. In order to solve the problems such as poor diversity, low quality and long training time of GAN in unsupervised environment, a new model with selective ensemble learning is proposed. Specifically, the discriminator in GAN is adopted in the form of integrated discrimination system, which can effectively reduce the discrimination error caused by the poor performance of single discriminator. Considering that if the integrated discriminant networks are set up in a unified network, each base discriminant network will tend to a form of expression in the model training. In order to encourage the diversity of discriminant network results and avoid the network falling into the same one, the discriminant networks with different network structures are set up. The majority voting strategy with dynamically adjusting the voting weight of the base discriminant network is introduced to vote the results of the integrated discriminant network. This has been shown to be effective in promoting model convergence and reducing experimental error significantly. Finally, the proposed model and the models in the same direction are evaluated with different evaluation indices under different datasets. Experimental results show that the proposed model is superior to several competitive models in terms of the diversity of generated samples, the quality of generated samples and the convergence speed of the model.

Key words: generative adversarial networks (GAN), integrated discrimination system, selective ensemble learning, majority voting strategy

摘要：

生成对抗网络（GAN）在图像生成方面具有广泛应用,但基于无监督方式与有监督方式的网络生成样本仍有较大差距。为解决生成对抗网络在无监督环境中生成样本多样性差、质量较低以及模型训练时间过长等问题,提出了具有选择性集成学习思想的生成对抗网络模型。将生成对抗网络中的判别网络采用集成判别系统的形式,有效减少了由单判别器判别性能不佳导致判别误差的情况;同时考虑到若集成判别网络均采用统一网络设置,则在模型训练中基判别网络将趋近于一种表现形式,为鼓励判别网络判别结果多样且避免网络陷入雷同,设置拥有不同网络结构的判别网络,并在集成判别网络中引入具有动态调整基判别网络投票权重的多数投票策略,对集成判别网络的判别结果进行投票,有效地促进了模型的收敛且较大减少了实验误差。最后将提出的模型与同方向的模型在不同数据集上使用不同评价指标进行评价,实验结果表明提出的模型无论在生成样本多样性、生成样本质量还是在模型收敛速度上均明显优于几种竞争模型。

关键词: 生成对抗网络（GAN）, 集成判别系统, 选择性集成学习, 多数投票策略