differences between Caffe and Keras when applying MAX pooling
<p>I was working on a project to convert keras json model to caffe prototxt
<br>
caffe supports arbitrary padding values<br>
keras (on top of tensorflow) supports 'same' and 'valid' values<br></p>

<p>For any padding value in caffe, we can manually add ZeroPadding layers in keras and then apply 'valid' scheme to get the same output dimensions<br></p>

<p>from 
<a href="https://github.com/MarcBS/keras/blob/master/keras/caffe/README.md" rel="nofollow noreferrer">https://github.com/MarcBS/keras/blob/master/keras/caffe/README.md</a><br></p>

<blockquote>
  <p>Given the differences between Caffe and Keras when applying the MAX pooling opperation, in some occasions the MAX pooling layers must include a <code>pad: 1</code> value even if they did not include them in their original .prototxt</p>
</blockquote>

<p>What exactly is the difference the implementation of MAX pooling between these two frameworks?</p>