@INPROCEEDINGS{MK12,
  AUTHOR =       {Iveta Mrázová and Marek Kukačka},
  TITLE =        {Can Deep Neural Networks Discover Meaningful Pattern Features?},
  BOOKTITLE =    {Procedia Computer Science},
  YEAR =         {2012},
  VOLUME =       {12}
  PAGES =        {194-199}
  ABSTRACT =     {Recent advances in the area of deep neural networks brought a lot of attention 
                  to some of the key issues important for their design. In particular for 2D-shapes, 
                  their accuracy has been shown to outperform all other classifiers - e.g., in the 
                  German Traffic Sign Competition run by IJCNN 2011. On the other hand, their 
                  training may be quite cumbersome and the structure of the network has to be 
                  chosen beforehand. This paper introduces a new sensitivity-based approach capable 
                  of picking the right image features from a pre-trained SOM-like feature detector. 
                  Experimental results obtained so far for hand-written digit recognition show that 
                  pruned network architectures impact a transparent representation of the features 
                  actually present in the data while improving network robustness.},
}