THE NEURAL NETWORK ZOO
POSTED ON SEPTEMBER 14, 2016 BY FJODOR VAN VEEN
With new neural network architectures popping up every now and then, it’s hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first.
随着新神经网络体系结构的不断涌现，很难对它们进行跟踪。知道所有的缩写(DCIGN, BiLSTM, DCGAN，还有其它更多的一些?)一开始可能有点压倒性。
So I decided to compose a cheat sheet containing many of those architectures. Most of these are neural networks, some are completely different beasts. Though all of these architectures are presented as novel and unique, when I drew the node structures… their underlying relations started to make more sense.
因此，我决定编写一个包含许多这样的体系结构的备忘单。其中大部分是神经网络，有些则是完全不同的beasts。虽然所有这些架构都是新颖独特的，但当我绘制节点结构时……它们的底层关系开始变得更有意义。
One problem with drawing them as node maps: it doesn’t really show how they’re used. For example, variational autoencoders (VAE) may look just like autoencoders (AE), but the training process is actually quite different. The use-cases for trained networks differ even more, because VAEs are generators, where you insert noise to get a new sample. AEs, simply map whatever they get as input to the closest training sample they “remember”. I should add that this overview is in no way clarifying how each of the different node types work internally (but that’s a topic for another day).
将它们绘制为节点映射有一个问题：它并没有真正显示如何使用它们。例如，变分自编码器(VAE)可能看起来就像自编码器(AE)，但是训练过程实际上是非常不同的。经过训练的网络的用例差别甚至更大，因为VAEs是生成器，您可以在其中插入噪声以获得新的样本。AEs，简单地将他们得到的输入映射到他们“记得”的最近的训练样本。我应该补充一点，这个概述并没有阐明每种不同的节点类型如何在内部工作(但这是另一个主题)。
It should be noted that while most of the abbreviations used are generally accepted, not all of them are. RNNs sometimes refer to recursive neural networks, but most of the time they refer to recurrent neural networks. That’s not the end of it though, in many places you’ll find RNN used as placeholder for any recurrent architecture, including LSTMs, GRUs and even the bidirectional variants. AEs suffer from a similar problem from time to time, where VAEs and DAEs and the like are called simply AEs. Many abbreviations also vary in the amount of “N”s to add at the end, because you could call it a convolutional neural network but also simply a convolutional network (resulting in CNN or CN).
应该指出的是，虽然使用的大多数缩写词都被普遍接受，但并不是所有的缩写词都被接受。RNNs有时指recursive神经网络，但大多数时候指的是recurrent神经网络。不过，这还没完，在许多地方，您会发现RNN被用作任何循环体系结构的占位符，包括LSTMs、GRUs甚至双向变体。AEs有时会遇到类似的问题，其中VAEs和DAEs等被简单地称为AEs。许多缩写在结尾添加的“N”的数量也有所不同，因为您可以将其称为卷积神经网络，也可以简单地称为卷积网络(即CNN或CN)。
Composing a complete list is practically impossible, as new architectures are invented all the time. Even if published it can still be quite challenging to find them even if you’re looking for them, or sometimes you just overlook some. So while this list may provide you with some insights into the world of AI, please, by no means take this list for being comprehensive; especially if you read this post long after it was written.
组成一个完整的列表实际上是不可能的，因为新的体系结构一直在被发明。即使发表了，找到它们仍然是很有挑战性的，即使你正在寻找它们，或者有时你只是忽略了一些。因此，尽管这份清单可能会让你对人工智能的世界有一些了解，但请不要认为这份清单是全面的;特别是如果你在这篇文章写完很久之后才读它。
For each of the architectures depicted in the picture, I wrote a very, very brief description. You may find some of these to be useful if you’re quite familiar with some architectures, but you aren’t familiar with a particular one.
对于图中描述的每一个架构，我都写了一个非常非常简短的描述。如果您非常熟悉某些体系结构，您可能会发现其中一些非常有用，但是您不熟悉特定的体系结构。
perceptrons
