In few-shot continual learning for generative models, a target mode must be learned with limited samples without adversely affecting the previously learned modes. In this paper, we propose a new continual learning approach for conditional generative adversarial networks (cGAN) based on a new mode-affinity measure for generative modeling. Our measure is entirely based on the cGAN's discriminator and can identify the existing modes that are most similar to the target. Subsequently, we expand the continual learning model by including the target mode using a weighted label derived from those of the closest modes. To prevent catastrophic forgetting, we first generate labeled data samples using the cGAN's generator, and then train the cGAN model for the target mode while memory replaying with the generated data. Our experimental results demonstrate the efficacy of our approach in improving the generation performance over the baselines and the state-of-the-art approaches for various standard datasets while utilizing fewer training samples.

翻译：在生成模型的小样本持续学习中，目标模式需在有限样本下学习，同时不对先前已学模式造成不利影响。本文提出一种面向条件生成对抗网络（cGAN）的新型持续学习方法，该方法基于一种用于生成建模的新颖模式亲和度度量。该度量完全依赖于cGAN判别器，能够识别与目标模式最相似的现有模式。随后，我们通过引入源自最接近模式的加权标签来扩展持续学习模型，从而包含目标模式。为防止灾难性遗忘，我们首先利用cGAN的生成器生成带标签的数据样本，然后通过基于生成数据的记忆重放方式训练目标模式对应的cGAN模型。实验结果表明，在多种标准数据集上，本方法在利用更少训练样本的同时，相比基线方法和现有最先进方法显著提升了生成性能。