  We investigate algorithmic progress in image classification on ImageNet,
perhaps the most well-known test bed for computer vision. We estimate a model,
informed by work on neural scaling laws, and infer a decomposition of progress
into the scaling of compute, data, and algorithms. Using Shapley values to
attribute performance improvements, we find that algorithmic improvements have
been roughly as important as the scaling of compute for progress computer
vision. Our estimates indicate that algorithmic innovations mostly take the
form of compute-augmenting algorithmic advances (which enable researchers to
get better performance from less compute), not data-augmenting algorithmic
advances. We find that compute-augmenting algorithmic advances are made at a
pace more than twice as fast as the rate usually associated with Moore's law.
In particular, we estimate that compute-augmenting innovations halve compute
requirements every nine months (95\% confidence interval: 4 to 25 months).
