  Convolutional neural networks (CNNs) can now achieve human-level performance
on challenging object recognition tasks. CNNs are also the leading quantitative
models in terms of predicting neural and behavioral responses in visual
recognition tasks. However, there is a widely accepted critique of CNN models:
unlike newborn animals, which learn rapidly and efficiently, CNNs are thought
to be "data hungry," requiring massive amounts of training data to develop
accurate models for object recognition. This critique challenges the promise of
using CNNs as models of visual development. Here, we directly examined whether
CNNs are more data hungry than newborn animals by performing parallel
controlled-rearing experiments on newborn chicks and CNNs. We raised newborn
chicks in strictly controlled visual environments, then simulated the training
data available in that environment by constructing a virtual animal chamber in
a video game engine. We recorded the visual images acquired by an agent moving
through the virtual chamber and used those images to train CNNs. When CNNs
received similar visual training data as chicks, the CNNs successfully solved
the same challenging view-invariant object recognition tasks as the chicks.
Thus, the CNNs were not more data hungry than animals: both CNNs and chicks
successfully developed robust object models from training data of a single
object.
