Discriminative v.s.Generative

举例理解：

假设我们有两类动物，大象 ( y = 1) 和狗 ( y = 0)。x是动物的特征向量。

给定一个训练集，逻辑回归或感知器算法（基本上）试图找到一条直线——即决策边界——将大象和狗分开。然后，为了将新动物分类为大象或狗，它会检查它落在决策边界的哪一侧，并做出相应的预测。我们将这些称为判别学习算法。

这是一种不同的方法。首先，看大象，我们可以建立一个大象长什么样的模型。然后，看看狗，我们可以建立一个单独的模型来了解狗的样子。最后，为了对新动物进行分类，我们可以将新动物与大象模型进行匹配，并将其与狗模型进行匹配，看看新动物看起来更像大象还是更像我们在训练集中看到的狗. 我们称这些生成学习算法。

定义理解：

More formally, given a set of data instances X and a set of labels Y:

• Generative models capture the joint probability p(X, Y), or just p(X) if there are no labels.
• Discriminative models capture the conditional probability p(Y | X).

A generative model includes the distribution of the data itself, and tells you how likely a given example is. For example, models that predict the next word in a sequence are typically generative models (usually much simpler than GANs) because they can assign a probability to a sequence of words.

A discriminative model ignores the question of whether a given instance is likely, and just tells you how likely a label is to apply to the instance.

Note that this is a very general definition. There are many kinds of generative model. GANs are just one kind of generative model.