A solution to generalized learning from small training sets found in infant repeated visual experiences of individual objects

One-year-old infants rapidly form and generalize categories of the everyday objects they encounter. Here we provide evidence on infants daily-life visual experiences for 8 early-learned object categories. Using a corpus of infant head-camera images recorded at mealtimes (87 mealtimes captured by 14 infants), we measure the frequency of the unique instances of each category and the variability of the visual experiences of each instance. The distribution of instances is highly skewed, containing, for each infant and category, many images of the same few objects along with fewer images of other instances. Graph theoretic measures of the similarity structure for individual categories reveal a lumpy mix of high similarity and high variability, organized into multiple but interconnected clusters of high-similarity images. In computational experiments, we show that artificially-created training sets characterized by a lumpy distribution of similarities support generalization to novel instances after very few training experiences. We discuss implications for visual object recognition, and for learning more generally, by both humans and machines.