作者
Daniel Stanley Tan, Yong-Xiang Lin, Kai-Lung Hua
发表日期
2020/6/26
期刊
IEEE Transactions on Circuits and Systems for Video Technology
卷号
31
期号
4
页码范围
1526-1539
出版商
IEEE
简介
Current multi-domain image-to-image translation models assume a fixed set of domains and that all the data are always available during training. However, over time, we may want to include additional domains to our model. Existing methods either require re-training the whole model with data from all domains or require training several additional modules to accommodate new domains. To address these limitations, we present IncrementalGAN, a multi-domain image-to-image translation model that can incrementally learn new domains using only a single generator. Our approach first decouples the domain label representation from the generator to allow it to be re-used for new domains without any architectural modification. Next, we introduce a distillation loss that prevents the model from forgetting previously learned domains. Our model compares favorably against several state-of-the-art baselines.
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DS Tan, YX Lin, KL Hua - IEEE Transactions on Circuits and Systems for Video …, 2020