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GANs involve training two networks in an adversarial game, where each network's task depends on its adversary. Recently, several works have framed GAN training as an online or continual learning problem. We focus on the discriminator, which must perform classification under an (adversarially) shifting data distribution. When trained on sequential tasks, neural networks exhibit forgetting. For GANs, discriminator forgetting leads to training instability. To counter forgetting, we encourage thearXiv:1810.11598v2 fatcat:4ulxhadrnbdgrp7adx3urrwski