Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition.

ABSTRACT: Deep convolutional neural networks (DCNNs) have attracted much attention recently, and have shown to be able to recognize thousands of object categories in natural image databases. Their architecture is somewhat similar to that of the human visual system: both use restricted receptive fields, and a hierarchy of layers which progressively extract more and more abstracted features. Yet it is unknown whether DCNNs match human performance at the task of view-invariant object recognition, whether they make similar errors and use similar representations for this task, and whether the answers depend on the magnitude of the viewpoint variations. To investigate these issues, we benchmarked eight state-of-the-art DCNNs, the HMAX model, and a baseline shallow model and compared their results to those

SUBMITTER: Kheradpisheh SR 

PROVIDER: S-EPMC5013454 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature