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What I'd do is train it on a large diverse set of foreign objects, then test it with a different diverse set of foreign objects, to see if it is possible to mak
by weregiraffe 8y ago
What I'd do is train it on a large diverse set of foreign objects, then test it with a different diverse set of foreign objects, to see if it is possible to make it generally robust. Basically, the goal is that any feature which is not overlapping with the "elephant" should not be influenced by it.
- YeGoblynQueenne 8y agoWell, CNNs are already trained on "a large diverse set" of objects, in many different configurations. By the way, something that is not described in the article- the referenced paper also describes experiments where objects where translated inside the source image itself - so not in another image. And the tested networks were still confused. So it's not "foreign" objects that are the problem. It's that neural nets generalise very poorly. Show them something they haven't seen before and their ability to recognise it falls off a cliff.