Individual differences among deep neural network models.

ABSTRACT: Deep neural networks (DNNs) excel at visual recognition tasks and are increasingly used as a modeling framework for neural computations in the primate brain. Just like individual brains, each DNN has a unique connectivity and representational profile. Here, we investigate individual differences among DNN instances that arise from varying only the random initialization of the network weights. Using tools typically employed in systems neuroscience, we show that this minimal change in initial conditions prior to training leads to substantial differences in intermediate and higher-level network representations despite similar network-level classification performance. We locate the origins of the effects in an under-constrained alignment of category exemplars, rather than misaligned category ce

SUBMITTER: Mehrer J 

PROVIDER: S-EPMC7665054 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature