It is meant to do well with input image pairs that may look completely different visually, but have some semantic components that are similar.

它在那些外表看起来可能完全不同、但有一些相似语义成分的一组输入图像上实现得很好。

This means that if we convert a summer image to a winter image, and then back to a summer image, we should get the very same input image back.

这意味着如果我们将夏季图像转换为冬季图像 然后再回到夏季图像，我们应该获得相同的输入图像。

This one is about image translation, which means that the input is an image, and the output is a different version of this input image that is changed according to our guidelines.

这一篇是关于图像转换的 它意味着输入一个图像，再根据我们的指示 输出这个输入图像的不同版本。

If you remember, in an earlier episode, we talked about an excellent work by Andrej Karpathy, who built an algorithm that looked at an input image, and described in a full, well-formed sentence what is depicted there.

不知你是否记得，在之前的一集里，我们提到了 Andrej Karpathy 的一篇杰出论文，他创造了一个算法，通过一句完整详尽的话来描述输入的图片。

This piece of work takes one single input image that we can trivially obtain by making a screenshot of the user interface, and it almost immediately provides us with the code that is required to recreate it.

这篇论文通过将 UI 界面截图、获得单一输入图像，就能快速生成所需的新代码。

The weapon of choice for this work was a Convolutional Neural Network, and the dataset the algorithm was trained on couldn't be simpler: it was given a large database of 2D input image and 3D output geometry pairs.

这项工作选择的算法是卷积神经网络，并且常用算法的数据集已经被精简的不能再简单了：它只有一个大的2D输入图像数据库，搭配3D几何输出模型。