  Humans have a remarkably large capacity to store detailed visual information
in long-term memory even after a single exposure, as demonstrated by classic
experiments in psychology. For example, Standing (1973) showed that humans
could recognize with high accuracy thousands of pictures that they had seen
only once a few days prior to a recognition test. In deep learning, the primary
mode of incorporating new information into a model is through gradient descent
in the model's parameter space. This paper asks whether deep learning via
gradient descent can match the efficiency of human visual long-term memory to
incorporate new information in a rigorous, head-to-head, quantitative
comparison. We answer this in the negative: even in the best case, models
learning via gradient descent require approximately 10 exposures to the same
visual materials in order to reach a recognition memory performance humans
achieve after only a single exposure. Prior knowledge induced via pretraining
and bigger model sizes improve performance, but these improvements are not very
visible after a single exposure (it takes a few exposures for the improvements
to become apparent), suggesting that simply scaling up the pretraining data
size or model size might not be a feasible strategy to reach human-level memory
efficiency.
