A high-throughput screening approach to discovering good forms of biologically inspired visual representation.

ABSTRACT: While many models of biological object recognition share a common set of "broad-stroke" properties, the performance of any one model depends strongly on the choice of parameters in a particular instantiation of that model--e.g., the number of units per layer, the size of pooling kernels, exponents in normalization operations, etc. Since the number of such parameters (explicit or implicit) is typically large and the computational cost of evaluating one particular parameter set is high, the space of possible model instantiations goes largely unexplored. Thus, when a model fails to approach the abilities of biological visual systems, we are left uncertain whether this failure is because we are missing a fundamental idea or because the correct "parts" have not been tuned correctly, assembled a

SUBMITTER: Pinto N 

PROVIDER: S-EPMC2775908 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature