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：在小样本持续学习的生成模型中，需在有限样本条件下学习目标模式，同时不损害先前已学习的模式。本文提出了一种基于新型生成建模模式亲和度度量的条件生成对抗网络（cGAN）持续学习方法。该度量完全基于cGAN的判别器，能够识别与目标最相似的现有模式。随后，我们通过结合目标模式扩展持续学习模型，具体采用源自最相似模式的加权标签。为避免灾难性遗忘，我们首先利用cGAN的生成器生成带标签数据样本，然后在记忆回放过程中使用这些生成数据训练目标模式的cGAN模型。实验结果表明，在多种标准数据集上，我们的方法在利用更少训练样本的情况下，相较于基线方法和现有最优方法，有效提升了生成性能。