[Paper Review]Distilling the Knowledge in a Neural Network,2015

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Analogy: Many insects have a larval form that is optimized for extracting energy and nutrients from the environment and a completely different adult form that is optimized for the very different requirements of travelling and reproduction.

The problem in Machine Learning: In large-scale machine learning, we typically use very similar models for the training stage and the deployment stage despite their very different requirements: For tasks like speech and object recognition, training must extract structure from very large, highly redundant datasets but it does not need to operate in real time and it can use a huge amount of computation. Deployment to a large number of users, however, has much more stringent requirements on latency and computational resources.

What is the knowledge in Neural Network?
A conceptual block that may have prevented more investigation of this very promising approach is that we tend to identify the knowledge in a trained model with the learned parameter values and this makes it hard to see how we can change the form of the model but keep the same knowledge. A more abstract view of the knowledge, that frees it from any particular instantiation, is that it is a learned mapping from input vectors to output vectors.

Why knowledge transfer? 
For tasks like MNIST in which the cumbersome model almost always produces the correct answer with very high confidence, much of the information about the learned function resides in the ratios of very small probabilities in the soft targets. For example, one version of a 2 may be given a probability of 10−6 of being a 3 and 10−9 of being a 7 whereas for another version it may be the other way around. This is valuable information that defines a rich similarity structure over the data (i. e. it says which 2’s look like 3’s and which look like 7’s) but it has very little influence on the cross-entropy cost function during the transfer stage because the probabilities are so close to zero.

Simple transfer:  An obvious way to transfer the generalization ability of the cumbersome model to a small model is to use the class probabilities produced by the cumbersome model as “soft targets” for training the small model.

Redefine the Softmax Function:
A term called Temperature T is added into Softmax Function as following. The larger the T is, the smoother the result probability matrix will be.

Distillation:
In the simplest form of distillation, knowledge is transferred to the distilled model by training it on a transfer set and using a soft target distribution for each case in the transfer set that is produced by using the cumbersome model with a high temperature in its softmax. The same high temperature is used when training the distilled model, but after it has been trained it uses a temperature of 1.

 https://arxiv.org/pdf/1503.02531.pdf

[Paper Review]Distilling the Knowledge in a Neural Network,2015

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原文地址：https://www.cnblogs.com/rhyswang/p/12235699.html