Correlation between examples
When I train the Network the Number of examples used for training vs. Cross-entropy, the performance of the Network it's not improving when adding new examples.


You are assuming improving new samples will always improve the performance. This is not true in many cases. Did you plot a learning curve to see how the performance is improving with respect to increasing number of samples? You probably should try to do this before delving deep into sample correlation.