论文标题
Microbatchgan：通过多反面歧视刺激多样性
microbatchGAN: Stimulating Diversity with Multi-Adversarial Discrimination
论文作者
论文摘要
我们建议通过使用多个歧视器并将每个Minibatch的不同部分（称为Microbatch）分配给每个歧视器，以解决生成对抗网络（GAN）中的模式崩溃问题。我们逐渐将每个歧视者的任务从区分实际和假样品之间，通过使用多样性参数$α$来区分来自内部或分配的微键的样本。然后，强迫发电机在每个Minibatch中促进多样性，以使微匹配歧视难以实现每个歧视器。因此，我们框架中的所有模型都受益于在生成的集合中减少各自损失的多样性。我们展示的证据表明，自多个数据集上的早期培训阶段以来，我们的解决方案会促进样本多样性。
We propose to tackle the mode collapse problem in generative adversarial networks (GANs) by using multiple discriminators and assigning a different portion of each minibatch, called microbatch, to each discriminator. We gradually change each discriminator's task from distinguishing between real and fake samples to discriminating samples coming from inside or outside its assigned microbatch by using a diversity parameter $α$. The generator is then forced to promote variety in each minibatch to make the microbatch discrimination harder to achieve by each discriminator. Thus, all models in our framework benefit from having variety in the generated set to reduce their respective losses. We show evidence that our solution promotes sample diversity since early training stages on multiple datasets.
