CFTS-GAN：面向生成对抗网络的持续少样本师生学习
CFTS-GAN: Continual Few-Shot Teacher Student for Generative Adversarial Networks
AI总结：
本文提出CFTS-GAN，通过师生蒸馏、适配器模块、跨域对应损失和冻结判别器策略，同时缓解GAN在少样本持续学习中的过拟合与灾难性遗忘问题。
摘要：
少样本学习和持续学习在生成对抗网络（GAN）中面临两个众所周知的挑战：过拟合和灾难性遗忘。在深度学习模型中，学习新任务会导致灾难性遗忘。在少样本设置下，模型从非常有限的样本数量（例如10个样本）中学习，这可能导致过拟合和模式崩溃。因此，本文提出了一种面向生成对抗网络的持续少样本师生技术（CFTS-GAN），同时考虑这两个挑战。我们的CFTS-GAN使用适配器模块作为学生来学习新任务，而不影响先前知识。为了使学生在学习新任务时更高效，教师模型的知识被蒸馏给学生。此外，教师和学生都使用跨域对应（CDC）损失来促进多样性并避免模式崩溃。而且，还利用了一种有效的冻结判别器策略来提升性能。定性和定量结果表明，图像合成更加多样，并且生成的定性样本与非常强大的最先进模型相比具有较好的竞争力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
