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Machine learning is advancing towards a data-science approach, implying anecessity to a line of investigation to divulge the knowledge learnt by deepneuronal networks. Limiting the comparison among networks merely to apredefined intelligent ability, according to ground truth, does not suffice, itshould be associated with innate similarity of these artificial entities. Here,we analysed multiple instances of an identical architecture trained to classifyobjects in static images (CIFAR and ImageNet data sets). We evaluated theperformance of the networks under various distortions and compared it to theintrinsic similarity between their constituent kernels. While we expected aclose correspondence between these two measures, we observed a puzzlingphenomenon. Pairs of networks whose kernels' weights are over 99.9% correlatedcan exhibit significantly different performances, yet other pairs with nocorrelation can reach quite compatible levels of performance. We showimplications of this for transfer learning, and argue its importance in ourgeneral understanding of what intelligence is, whether natural or artificial.

Paradox in Deep Neural Networks: Similar yet Different while Different yet Similar