title: Late fusion for the CNN features
body: <p>I am working on early and late fusion of CNN features. I have taken features from multiple layer of CNN. For the early fusion I have captured the feature of three different layers and then horizontally concatenate them <code>F= [F1' F2' F3'];</code> For the late Fusion I was reading this <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.78.5928&amp;rep=rep1&amp;type=pdf" rel="nofollow noreferrer">paper</a>. They have mentioned to do supervised learning twice. But couldn't understand the way.</p>
  
  <p>For example this is the image taken from the above mentioned paper. 
  The first image have three different features and for first supervised learning  the labels lets say will be 1 in 4 class image set. The output for example is [1 1 3]. Lets say the third classifier has wrong result. 
  Then my question is then the  multimodal feature concatenation is like [1 1 3] with the label 1 lets say for class 1 image?</p>
  
  <p><a href="https://i.stack.imgur.com/HlKfU.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/HlKfU.jpg" alt="enter image description here"></a></p>