Summary of Cfts-gan: Continual Few-shot Teacher Student For Generative Adversarial Networks, by Munsif Ali et al.
CFTS-GAN: Continual Few-Shot Teacher Student for Generative Adversarial Networks
by Munsif Ali, Leonardo Rossi, Massimo Bertozzi
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Continual Few-shot Teacher-Student technique (CFTS-GAN) addresses the challenges of overfitting and catastrophic forgetting in generative adversarial networks (GANs). The CFTS-GAN utilizes an adapter module as a student to learn new tasks without compromising previous knowledge. Knowledge distillation from a teacher model is used to make the student efficient, while Cross-Domain Correspondence (CDC) loss promotes diversity and avoids mode collapse. Freezing the discriminator enhances performance. The technique demonstrates more diverse image synthesis and produces high-quality samples comparable to state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps GANs learn new tasks without forgetting old ones. It uses a special student model that learns from a teacher model, which knows how to do many things already. This way, the student can learn new skills without messing up what it already knows. The method also helps prevent bad images by making sure they’re different and good quality. This is important because GANs are used in lots of applications like image generation. |
Keywords
» Artificial intelligence » Few shot » Gan » Image generation » Image synthesis » Knowledge distillation » Overfitting » Student model » Teacher model
