Reviews: Toddler-Inspired Visual Object Learning

The goal of the paper is to "data mine" records of toddlers' and their mothers' fixations while playing with a set of 24 toys in order to observe what might be good training data for a deep network, given a fixed training budget. The idea is that the toddler is the best visual learning system we know, and so the data that toddlers learn from should give us a clue about what data is appropriate for deep learning. They take fixation records extracted from toddlers (16-24 mo old) and their mothers collected via scene cameras and eye tracking to examine the data distribution of infants' visual input or mothers' visual input. This study clearly falls under the cognitive science umbrella at NIPS, although they try to make it about deep learning. For example, if they only cared about deep learning, they would not use a retinal filter. First, they manually collect data recording what toys the infants and mothers are fixating on (ignoring other fixations).