<p>A straight through estimator is a way of estimating gradients for a threshold operation in a neural network. The threshold could be as simple as the following function,</p>

<p><a href="https://i.stack.imgur.com/ECIIg.png" rel="noreferrer"><img src="https://i.stack.imgur.com/ECIIg.png" alt="enter image description here"></a></p>

<p>As we can see, the derivative of this threshold function will 0 and during back-propagation, the network will not learn anything since it gets 0 gradients and the weights won't get updated. </p>

<p>The concept of a straight through estimator is that you set the incoming gradients to a threshold function equal to it's outgoing gradients, disregarding the derivative of the threshold function itself. This has been shown to perform well in the results (Figure 2) in <a href="https://arxiv.org/pdf/1308.3432.pdf" rel="noreferrer">this</a> paper you have referenced.</p>
The concept of straight through estimator (STE)
<p>I have seen straight through estimator (STE) in many Neural Network related papers e.g. <a href="http://arxiv.org/pdf/1308.3432v1.pdf" rel="noreferrer">this</a> and <a href="https://arxiv.org/pdf/1606.06160.pdf" rel="noreferrer">this</a>. But I cannot understand the concept.  I wonder if anyone could explain STE or refer me to a simple resource? </p>