论文标题
线性无监督的图像到图像翻译的令人惊讶的有效性
The Surprising Effectiveness of Linear Unsupervised Image-to-Image Translation
论文作者
论文摘要
无监督的图像到图像翻译是一个固有的不良问题。基于深层编码器架构的最新方法显示出令人印象深刻的结果，但我们表明它们仅由于强烈的局部性偏见而成功，并且他们无法学习非常简单的非本地转换（例如，将倒置的面孔映射到直立的面孔）。当删除局部偏见时，方法太强大了，可能无法学习简单的本地转换。在本文中，我们介绍了无监督图像的线性编码器架构，以进行图像翻译。我们表明，通过这些体系结构，学习更加容易，更快，但是结果令人惊讶地有效。特别是，我们显示了许多局部问题，这些问题是线性方法的结果与最先进的体系结构的结果相当，但培训时间的一小部分以及许多非局部问题的问题，而线性方法成功。
Unsupervised image-to-image translation is an inherently ill-posed problem. Recent methods based on deep encoder-decoder architectures have shown impressive results, but we show that they only succeed due to a strong locality bias, and they fail to learn very simple nonlocal transformations (e.g. mapping upside down faces to upright faces). When the locality bias is removed, the methods are too powerful and may fail to learn simple local transformations. In this paper we introduce linear encoder-decoder architectures for unsupervised image to image translation. We show that learning is much easier and faster with these architectures and yet the results are surprisingly effective. In particular, we show a number of local problems for which the results of the linear methods are comparable to those of state-of-the-art architectures but with a fraction of the training time, and a number of nonlocal problems for which the state-of-the-art fails while linear methods succeed.
