CODEWITHSUNDEEP
neural-networkdeep-learning
Aaron_Geng
Aaron_Geng

Reputation: 339

How to pre-train a deep neural network (or RNN) with unlabeled data?

Recently, I was asked about how to pre-train a deep neural network with unlabeled data, meaning, instead of initializing the model weight with small random numbers, we set initial weight from a pretrained model (with unlabeled data).

Well, intuitively, I kinda get it, it probably helps with the vanishing gradient issue and shorten the training time when there are not too much labeled data available. But still, I don't really know how it is done, how can you train a neural network with unlabeled data? Is it something like SOM or Boltzmann machine?

Has anybody heard about this? If yes, can you provide some links to sources or papers. I am curious. Greatly appreciate!

Upvotes: 0

Views: 598

Answers (1)

user3390629
user3390629

Reputation: 854

There are lots of ways to deep-learn from unlabeled data. Layerwise pre-training was developed back in the 2000s by Geoff Hinton's group, though that's generally fallen out of favor.

More modern unsupervised deep learning methods include Auto-Encoders, Variational Auto-Encoders, and Generative Adversarial Networks. I won't dive into the details of all of them, but the simplest of these, auto-encoders, work by compressing an unlabeled input into a low dimensional real-valued representation, and using this compressed representation to reconstruct the original input. Intuitively, a compressed code that can effectively be used to recreate an input is likely to capture some useful features of said input. See here for an illustration and more detailed description. There are also plenty of examples implemented in your deep learning library of choice.

I guess in some sense any of the listed methods could be used as pre-training, e.g for preparing a network for a discriminative task like classification, though I'm not aware of that being a particularly common practice. Initialization methods, activation functions, and other optimization tricks are generally advanced enough to do well without more complicated initialization procedures.

Upvotes: 2

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