  Young children develop sophisticated internal models of the world based on
their visual experience. Can such models be learned from a child's visual
experience without strong inductive biases? To investigate this, we train
state-of-the-art neural networks on a realistic proxy of a child's visual
experience without any explicit supervision or domain-specific inductive
biases. Specifically, we train both embedding models and generative models on
200 hours of headcam video from a single child collected over two years and
comprehensively evaluate their performance in downstream tasks using various
reference models as yardsticks. On average, the best embedding models perform
at a respectable 70% of a high-performance ImageNet-trained model, despite
substantial differences in training data. They also learn broad semantic
categories and object localization capabilities without explicit supervision,
but they are less object-centric than models trained on all of ImageNet.
Generative models trained with the same data successfully extrapolate simple
properties of partially masked objects, like their rough outline, texture,
color, or orientation, but struggle with finer object details. We replicate our
experiments with two other children and find remarkably consistent results.
Broadly useful high-level visual representations are thus robustly learnable
from a representative sample of a child's visual experience without strong
inductive biases.
