This study reveals what kind of temporal and spatial patterns form when learning in an adversarial relationship between two individuals. The model was implemented by coupling generative adversarial networks, which are well-known in the field of machine learning. The obtained temporal patterns resulted in chaos with a positive Lyapunov exponent for time-series learning, whereas spatial pattern learning produced structured patterns with a higher fractal dimension, not just more complexity with a higher entropy.

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© 2022 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license
2022
Massachusetts Institute of Technology
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Issue Section:
General Conference