Small sample learning ---- semi-supervised learning algorithm

Others 2019-09-04 02:46:23 views: null

https://blog.csdn.net/mao_feng/article/details/78939864

In real life, we have encountered a small amount of sample labels, and a large number of samples without labels, how to do this deal with it?

Method 1: Migration finetune learning

Looking for similar universal data set to train the network, by modifying the rear layer 2 or layer 3 network, migration study done, to fine tune the parameters of the network, thus training the model.

Method 2:

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Origin www.cnblogs.com/ivyharding/p/11455996.html

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