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Poster
Self-supervised learning through the eyes of a child
Emin Orhan · Vaibhav Gupta · Brenden Lake

Thu Dec 10 09:00 AM -- 11:00 AM (PST) @ Poster Session 5 #1336

Within months of birth, children develop meaningful expectations about the world around them. How much of this early knowledge can be explained through generic learning mechanisms applied to sensory data, and how much of it requires more substantive innate inductive biases? Addressing this fundamental question in its full generality is currently infeasible, but we can hope to make real progress in more narrowly defined domains, such as the development of high-level visual categories, thanks to improvements in data collecting technology and recent progress in deep learning. In this paper, our goal is precisely to achieve such progress by utilizing modern self-supervised deep learning methods and a recent longitudinal, egocentric video dataset recorded from the perspective of three young children (Sullivan et al., 2020). Our results demonstrate the emergence of powerful, high-level visual representations from developmentally realistic natural videos using generic self-supervised learning objectives.

Author Information

Emin Orhan (New York University)
Vaibhav Gupta (New York University)
Brenden Lake (New York University)

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