Unpaired image-to-image translation (UNIT) aims to map images between two
visual domains without paired training data. However, given a UNIT model
trained on certain domains, it is difficult for current methods to incorporate
new domains because they often need to train the full model on both existing
and new domains. To address this problem, we propose a new domain-scalable UNIT
method, termed as latent space anchoring, which can be efficiently extended to
new visual domains and does not need to fine-tune encoders and decoders of
existing domains. Our method anchors images of different domains to the same
latent space of frozen GANs by learning lightweight encoder and regressor
models to reconstruct single-domain images. In the inference phase, the learned
encoders and decoders of different domains can be arbitrarily combined to
translate images between any two domains without fine-tuning. Experiments on
various datasets show that the proposed method achieves superior performance on
both standard and domain-scalable UNIT tasks in comparison with the
state-of-the-art methods.

提出了一种新颖的图像生成方法：latent space anchoring，可以实现在不需要调整现有域的编解码器的情况下扩展到新的视觉域，该方法通过学习轻量级编码器和回归器来将不同域的图像锚定到相同的冻结 GANs 的潜在空间中，并且其编码器和解码器可以任意组合进行图像翻译，实验表明，该方法在标准和可伸缩的 UNIT 任务上均表现优异。

通过潜空间定位的领域可扩展非配对图像翻译

Domain-Scalable Unpaired Image Translation via Latent Space Anchoring

How to build and use dialogue data efficiently, and how to deploy models in
different domains at scale can be two critical issues in building a
task-oriented dialogue system. In this paper, we propose a novel manual-guided
dialogue scheme to alleviate these problems, where the agent learns the tasks
from both dialogue and manuals. The manual is an unstructured textual document
that guides the agent in interacting with users and the database during the
conversation. Our proposed scheme reduces the dependence of dialogue models on
fine-grained domain ontology, and makes them more flexible to adapt to various
domains. We then contribute a fully-annotated multi-domain dataset MagDial to
support our scheme. It introduces three dialogue modeling subtasks: instruction
matching, argument filling, and response generation. Modeling these subtasks is
consistent with the human agent's behavior patterns. Experiments demonstrate
that the manual-guided dialogue scheme improves data efficiency and domain
scalability in building dialogue systems. The dataset and benchmark will be
publicly available for promoting future research.

本文提出了一种新的手动引导对话方案，其中代理从对话和手册学习任务，从而降低了对领域本体的依赖，使其更灵活地适应不同的领域，相关实验表明该方案提高了对话系统的数据效率和领域可扩展性