How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?

1 code implementation • NeurIPS 2022 • Chengxu Zhuang, Violet Xiang, Yoon Bai, Xiaoxuan Jia, Nicholas Turk-Browne, Kenneth Norman, James J. DiCarlo, Daniel LK Yamins

Taken together, our benchmarks establish a quantitative way to directly compare learning between neural networks models and human learners, show how choices in the mechanism by which such algorithms handle sample comparison and memory strongly impact their ability to match human learning abilities, and expose an open problem space for identifying more flexible and robust visual self-supervision algorithms.

Diversity Self-Supervised Learning