基于经验回放的个性化生成式人脸模型持续学习
Continual Learning of Personalized Generative Face Models with Experience Replay
AI总结：
针对个性化2D/3D生成式人脸模型顺序更新中的灾难性遗忘问题，提出结合随机采样与StyleGAN潜空间的凸包经验回放算法，在小存储长期部署下优于随机回放基线。
摘要：
我们提出了一个新的持续学习问题：当定期采集到具有不同外观、风格、姿态和光照的新批次照片时，如何按顺序更新个性化2D和3D生成式人脸模型的权重。我们观察到，对模型进行简单的顺序微调会导致对个体面部过去表征的灾难性遗忘。随后我们证明，当能够存储并重放相对较多的图像时，一种简单的基于随机采样的经验回放方法可有效缓解灾难性遗忘。然而，为了在存储相对较小的情况下长期部署这些模型，这种简单的基于随机采样的回放技术同样会遗忘过去的表征。因此，我们提出了一种新的经验回放算法，将随机采样与StyleGAN的潜空间相结合，把缓冲区表示为最优凸包。我们观察到，与随机采样基线和下界相比，所提出的基于凸包的经验回放能更有效地防止遗忘。
英文摘要：
We introduce a novel continual learning problem: how to sequentially update the weights of a personalized 2D and 3D generative face model as new batches of photos in different appearances, styles, poses, and lighting are captured regularly. We observe that naive sequential fine-tuning of the model leads to catastrophic forgetting of past representations of the individual's face. We then demonstrate that a simple random sampling-based experience replay method is effective at mitigating catastrophic forgetting when a relatively large number of images can be stored and replayed. However, for long-term deployment of these models with relatively smaller storage, this simple random sampling-based replay technique also forgets past representations. Thus, we introduce a novel experience replay algorithm that combines random sampling with StyleGAN's latent space to represent the buffer as an optimal convex hull. We observe that our proposed convex hull-based experience replay is more effective in preventing forgetting than a random sampling baseline and the lower bound.
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。
