We address a challenging lifelong few-shot image generation task for the
first time. In this situation, a generative model learns a sequence of tasks
using only a few samples per task. Consequently, the learned model encounters
both catastrophic forgetting and overfitting problems at a time. Existing
studies on lifelong GANs have proposed modulation-based methods to prevent
catastrophic forgetting. However, they require considerable additional
parameters and cannot generate high-fidelity and diverse images from limited
data. On the other hand, the existing few-shot GANs suffer from severe
catastrophic forgetting when learning multiple tasks. To alleviate these
issues, we propose a framework called Lifelong Few-Shot GAN (LFS-GAN) that can
generate high-quality and diverse images in lifelong few-shot image generation
task. Our proposed framework learns each task using an efficient task-specific
modulator - Learnable Factorized Tensor (LeFT). LeFT is rank-constrained and
has a rich representation ability due to its unique reconstruction technique.
Furthermore, we propose a novel mode seeking loss to improve the diversity of
our model in low-data circumstances. Extensive experiments demonstrate that the
proposed LFS-GAN can generate high-fidelity and diverse images without any
forgetting and mode collapse in various domains, achieving state-of-the-art in
lifelong few-shot image generation task. Surprisingly, we find that our LFS-GAN
even outperforms the existing few-shot GANs in the few-shot image generation
task. The code is available at Github.

提出了一种名为 LFS-GAN 的框架，用于解决在终身少样本图像生成任务中遇到的灾难性遗忘和过拟合问题，并通过引入 LeFT 和模式寻求损失方法实现了高质量、多样性图像生成，成为同领域的最新研究成果。