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

We propose UniT, a Unified Transformer model to simultaneously learn the most
prominent tasks across different domains, ranging from object detection to
natural language understanding and multimodal reasoning. Based on the
transformer encoder-decoder architecture, our UniT model encodes each input
modality with an encoder and makes predictions on each task with a shared
decoder over the encoded input representations, followed by task-specific
output heads. The entire model is jointly trained end-to-end with losses from
each task. Compared to previous efforts on multi-task learning with
transformers, we share the same model parameters across all tasks instead of
separately fine-tuning task-specific models and handle a much higher variety of
tasks across different domains. In our experiments, we learn 7 tasks jointly
over 8 datasets, achieving strong performance on each task with significantly
fewer parameters. Our code is available in MMF at this https URL

我们提出了一种名为 UniT 的统一 Transformer 模型，该模型可以同时学习不同领域中最重要的任务，从目标检测到自然语言理解和多模态推理，在编码器 - 解码器架构的基础上，通过编码器对每个输入模态进行编码，并使用共享解码器对编码的输入表示进行每个任务的预测，然后是特定于任务的输出头。我们的实验证明，在 8 个数据集上联合学习 7 个任务，使用比以前的工作更少的参数，在每个任务上都获得了强大的性能。