The main challenge in continual learning for generative models is to effectively learn new target modes with limited samples while preserving previously learned ones. To this end, we introduce a new continual learning approach for conditional generative adversarial networks by leveraging a mode-affinity score specifically designed for generative modeling. First, the generator produces samples of existing modes for subsequent replay. The discriminator is then used to compute the mode similarity measure, which identifies a set of closest existing modes to the target. Subsequently, a label for the target mode is generated and given as a weighted average of the labels within this set. We extend the continual learning model by training it on the target data with the newly-generated label, while performing memory replay to mitigate the risk of catastrophic forgetting. Experimental results on benchmark datasets demonstrate the gains of our continual learning approach over the state-of-the-art methods, even when using fewer training samples.

翻译：持续学习在生成模型中的主要挑战是如何在保留已学习模式的同时，利用有限样本有效学习新的目标模式。为此，我们提出一种专为生成建模设计的模式亲和度评分方法，并将其应用于条件生成对抗网络的持续学习。首先，生成器生成现有模式的样本用于后续回放。随后，利用判别器计算模式相似度度量，从而识别与目标最相近的一组现有模式。接着，为目标模式生成标签，该标签为该组内标签的加权平均值。通过使用新生成的标签训练目标数据，并执行记忆回放以降低灾难性遗忘风险，我们对持续学习模型进行了扩展。在基准数据集上的实验结果表明，即使使用更少的训练样本，我们的持续学习方法仍优于现有最优方法。