  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 of humans with backward masking. Unlike in all
previous DCNN studies, we carefully controlled the magnitude of the viewpoint
variations to demonstrate that shallow nets can outperform deep nets and humans
when variations are weak. When facing larger variations, however, more layers
were needed to match human performance and error distributions, and to have
representations that are consistent with human behavior. A very deep net with
18 layers even outperformed humans at the highest variation level, using the
most human-like representations.
