Transformation Invariant Autoassociation with Application to Handwritten Character Recognition
Authors: Holger Schwenk, Maurice Milgram
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: When  training  neural  networks  by  the classical  backpropagation algo(cid:173) rithm the whole problem to learn must be expressed by a set of inputs and  desired  outputs.  However,  we often  have  high-level knowledge  about  the  learning problem.  In  optical character recognition  (OCR),  for  in(cid:173) stance, we know that the classification should be invariant under a set of  transformations like rotation or translation.  We propose a new modular  classification system based on several autoassociative multilayer percep(cid:173) trons which allows the efficient incorporation of such knowledge.  Results  are reported on the NIST database of upper case handwritten letters and  compared to other approaches to the invariance problem. 
1  INCORPORATION OF EXPLICIT KNOWLEDGE 
The  aim  of supervised  learning is  to  learn  a  mapping  between  the  input and  the  output  space from  a  set of example pairs (input,  desired output).  The classical implementation  in the domain of neural networks is the backpropagation algorithm.  If this learning set is  sufficiently representative of the underlying data distributions, one hopes that after learning,  the system is able to generalize correctly to other inputs of the same distribution. 
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Holger Schwenk,  Maurice  Milgram 
It  would  be  better to  have  more powerful techniques  to  incorporate knowledge  into  the  learning process  than  the choice of a set of examples.  The use  of additional knowledge  is  often limited  to  the  feature  extraction  module.  Besides  simple  operations  like  (size)  normalization,  we  can  find  more sophisticated  approaches  like  zernike  moments  in  the  domain of optical character recognition (OCR). In  this paper we  will  not investigate this  possibility, all discussed classifiers work directly on almost non preprocessed data (pixels). 
In the context of OCR interest focuses  on invariance of the classifier under a  number of  given transformations (translation, rotation, ... ) of the data to classify.  In general a neural  network could extract those properties of a large enough learning set, but it is very hard to  learn and will probably take a lot of time.  In the last years  two  main approaches for this  invariance problem have been proposed:  tangent-prop and tangent-distance.  An indirect  incorporation can be achieved by boosting (Drucker, Schapire and Simard, 1993). 
In this paper we briefly discuss these approaches and will present a new classification system  which allows the efficient incorporation of transformation invariances. 
1.1  TANGENT PROPAGATION 
The principle of tangent-prop is to specify besides desired outputs also desired changes jJJ.  of the output vector when transforming the net input x  by the transformations tJJ.  (Simard,  Victorri, LeCun and Denker, 1992).  For this, let us define a transformation of pattern p as t(p, a) : P  --t  P  where P  is the space  of all  patterns  and a  a parameter.  Such  transformations are in  general highly  nonlinear  operations in  the pixel space P  and their analytical expressions are  seldom known.  It is  therefore favorable to use a first order approximation: