Generative Adversarial Networks (GANs) are a class of generative models used for various applications, but they have been known to suffer from the mode collapse problem, in which some modes of the target distribution are ignored by the generator. Investigative study using a new data generation procedure indicates that the mode collapse of the generator is driven by the discriminator's inability to maintain classification accuracy on previously seen samples, a phenomenon called Catastrophic Forgetting in continual learning. Motivated by this observation, we introduce a novel training procedure that adaptively spawns additional discriminators to remember previous modes of generation. On several datasets, we show that our training scheme can be plugged-in to existing GAN frameworks to mitigate mode collapse and improve standard metrics for GAN evaluation.
翻译:创基因网络(GANs)是用于各种应用的一组基因模型,但已知它们受到模式崩溃问题的影响,其中发电机忽视了目标分布的某些模式。使用新的数据生成程序进行的调查研究表明,造成发电机模式崩溃的原因是歧视者无法维持先前所见样品的分类准确性,这种现象被称为在持续学习中遗忘灾难。我们根据这一观察,引入了一种新的培训程序,在适应性上催生了更多的歧视者,以纪念以前的一代模式。我们在若干数据集中显示,我们的培训计划可以插进现有的GAN框架,以减轻模式崩溃和改进GAN评估的标准指标。