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作者：bestrivern
来源：CSDN
原文：https://blog.csdn.net/bestrivern/article/details/87008263
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一.迁移学习(Transfer learning)
1.Task A and Task B has the same input x
2.You have a lot more data for Task A than Task B
3.Low level features from A could be helpful for learning B
（感觉上面的第一点说的好像不太对， 所以 ，ps: point 1 is conflict with point 2, maybe point 1 should be task A has input x and task B has input y, input x is similar with input y）
二.多任务学习(Multitask learning)
1.Training on a set of tasks that could benefit from having shared low-level features
2.Usually:Amount of data you have for each task is quite similar
3.Can train a big enough neural network to do well on all the tasks
三.端到端学习(End-to-end deep learning)
Pros:
Let the data speak
Less hand-designing of components needed
Cons:
May need large amount of data
Excludes potentially useful hand-designed components
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