解决生成模型少样本持续学习中的过拟合与遗忘问题。
CFTS-GAN: Continual Few-Shot Teacher Student for Generative Adversarial Networks
- 用教师-学生框架+适配模块，避免新任务干扰旧知识。
- 通过跨域对应损失提升图像多样性，缓解模式崩溃。
- 冻结判别器增强性能，适合持续学习场景的生成应用。
少样本与持续学习在生成对抗网络中面临过拟合和灾难性遗忘两大挑战。新任务学习会导致深度模型遗忘旧知识；而少样本设置下（如仅10个样本）易引发过拟合与模式崩溃。本文提出连续少样本教师-学生生成模型（CFTS-GAN），协同应对上述问题。采用适配模块作为学生模型，在不破坏已有知识的前提下学习新任务。通过知识蒸馏将教师模型的知识传递给学生，提升学习效率。同时，师生模型均使用跨域对应（CDC）损失，促进生成多样性并防止模式崩溃。此外，采用有效策略冻结判别器以增强性能。定性与定量结果表明，该方法生成图像更具多样性，且样本质量媲美甚至优于现有先进模型。
原文摘要 · Abstract (English)
Few-shot and continual learning face two well-known challenges in GANs: overfitting and catastrophic forgetting. Learning new tasks results in catastrophic forgetting in deep learning models. In the case of a few-shot setting, the model learns from a very limited number of samples (e.g. 10 samples), which can lead to overfitting and mode collapse. So, this paper proposes a Continual Few-shot Teacher-Student technique for the generative adversarial network (CFTS-GAN) that considers both challenges together. Our CFTS-GAN uses an adapter module as a student to learn a new task without affecting the previous knowledge. To make the student model efficient in learning new tasks, the knowledge from a teacher model is distilled to the student. In addition, the Cross-Domain Correspondence (CDC) loss is used by both teacher and student to promote diversity and to avoid mode collapse. Moreover, an effective strategy of freezing the discriminator is also utilized for enhancing performance. Qualitative and quantitative results demonstrate more diverse image synthesis and produce qualitative samples comparatively good to very stronger state-of-the-art models.
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