  Recent research has seen many behavioral comparisons between humans and deep
neural networks (DNNs) in the domain of image classification. Often, comparison
studies focus on the end-result of the learning process by measuring and
comparing the similarities in the representations of object categories once
they have been formed. However, the process of how these representations emerge
-- that is, the behavioral changes and intermediate stages observed during the
acquisition -- is less often directly and empirically compared. Here we report
a detailed investigation of the learning dynamics in human observers and
various classic and state-of-the-art DNNs. We develop a constrained supervised
learning environment to align learning-relevant conditions such as starting
point, input modality, available input data and the feedback provided. Across
the whole learning process we evaluate and compare how well learned
representations can be generalized to previously unseen test data. Comparisons
across the entire learning process indicate that DNNs demonstrate a level of
data efficiency comparable to human learners, challenging some prevailing
assumptions in the field. However, our results also reveal representational
differences: while DNNs' learning is characterized by a pronounced
generalisation lag, humans appear to immediately acquire generalizable
representations without a preliminary phase of learning training set-specific
information that is only later transferred to novel data.
