# Examples: t-c.in

This is the problem of recognizing a T or a C irrespective of location or orientation that is introduced on page 348 of Parallel Distributed Processing, Explorations in the Microstructure of Cognition, Vol. 1, edited by David Rumelhart and James McClelland.

It isn't hard for the network to learn the training set, but it is very hard for what it learns to transfer to the testing set, which contains some held-out examples. The solution given in the PDP book was to have each hidden unit look at a particular patch of the input and to tie their weights together so they all compute the same function.

This seems a bit heavy-handed. Can you come up with a different way to get the network to generalize from its experiences? I haven't solved this, so let me know if you come up with a solution. You might try writing a script to form local patterns of projections from the input to the hidden layer.

But this problem raises the question: Is our ability to recognize objects really translation-invariant? Are you actually good at recognizing objects that you aren't fixating on? How about images of people you know? Have a friend test you. I'm willing to bet the brain isn't so good at translation-invariance either.

Last modified: Thu Nov 16 15:04:43 EST 2000