第七课: 手写Resnets; U-net; Generative (adversarial) networks
Overview 综述
In the final lesson of Practical Deep Learning for Coders we’ll study one of the most important techniques in modern architectures: the skip connection. This is most famously used in the resnet, which is the architecture we’ve used throughout this course for image classification, and appears in many cutting edge results. We’ll also look at the U-net architecture, which uses a different type of skip connection to greatly improve segmentation results (and also for similar tasks where the output structure is similar to the input).
在这最后一节课里，我们将学到最新结构设计中最重要的一个技术：skip connection 。这项技术诞生于resnet, 也就是我们在做图片分类时一直在用的模型框架，为我们带来了顶级的表现。我们还会学习U-net结构，这项技术采用另一种skip connection，极大地改进了segmentation的效果（这项技术同样适用于其他任务，只要他们的输入值和输出值结构相似）。
We’ll then use the U-net architecture to train a super-resolution model. This is a model which can increase the resolution of a low-quality image. Our model won’t only increase resolution—it will also remove jpeg artifacts, and remove unwanted text watermarks.
之后我们将用U-net训练 super-resolution 模型。这个模型能够提升低像素图片的清晰度。我们的模型不仅可以提升像素，还能去除jpeg格式的杂物，以及消除文字水印。
In order to make our model produce high quality results, we will need to create a custom loss function which incorporates feature loss (also known as perceptual loss), along with gram loss. These techniques can be used for many other types of image generation task, such as image colorization.
为了让模型生成高质量输出，我们需要设计一款特制损失函数来将特征损失值（也就是所谓的perceptual loss)和gram 损失值做融合。这些技巧同样适用于很多其他的图片生成任务，比如图片上色。
Finally, we’ll learn about a recent loss function known as generative adversarial loss (used in generative adversarial networks, or GANs), which can improve the quality of generative models in some contexts, at the cost of speed.
最后，我们讲学习一个很新的损失函数，被称为generative adversarial loss (被用于生成对抗网络模型，也就是GANs），它能改进生成模型在某任务背景下的表现，但速度上会有所牺牲。
The techniques we show in this lesson include some unpublished research that:
- Let us train GANs more quickly and reliably than standard approaches, by leveraging transfer learning
- Combines architectural innovations and loss function approaches that haven’t been used in this way before.
The results are stunning, and train in just a couple of hours (compared to previous approaches that take a couple of days).
我们在本课中展示但还未正式做学术发表的技巧，如下：
- 利用迁移学习，做到比常规方法更快更可靠的训练GANs
- 在结构设计创新与损失函数使用方法的融合上，也做出了史无前例的创新
对比之前常规方法需要数天时间，我们只是训练了数小时，模型表现已经非常靓丽。
Lesson Resources 课程资源
- 非常详尽的第七课课程笔记 - 感谢 @hiromi
- Notebooks:
- Lesson 7 in-class discussion thread
- Lesson 7 advanced discussion
Other Resources 其他资源
- 可视化神经网络的损失值的地表风景图 论文
- Convolution arithmetic 在课程中展示的论文
- Perceptual Losses for Real-Time Style Transfer and Super-Resolution 论文
- Github对Jeremy的采访
-
ipyexperiments - @stas 提供了比
gc.collect更便捷的lib帮助释放你的GPU内存 - Documentation improvements thread (请帮助我们一起将文档做的更好!)