Abstract
Behavior identification and quantification techniques have undergone rapid development. To this end, supervised or unsupervised methods are chosen based upon their intrinsic strengths and weaknesses (e.g. user bias, training cost, complexity, action discovery). Here, a new active learning platform, A-SOiD, blends these strengths and in doing so, overcomes several of their inherent drawbacks. A-SOiD iteratively learns user-defined groups with a fraction of the usual training data while attaining expansive classification through directed unsupervised classification. In socially-interacting mice, A-SOiD outperformed standard methods despite requiring 85% less training data. Additionally, it isolated two additional ethologically-distinct mouse interactions via unsupervised classification. Similar performance and efficiency was observed using non-human primate 3D pose data. In both cases, the transparency in A-SOiD’s cluster definitions revealed the defining features of the supervised classification through a game-theoretic approach. To facilitate use, A-SOiD comes as an intuitive, open-source interface for efficient segmentation of user-defined behaviors and discovered subactions.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
The acknowledgments, method section, and references were revised to reflect the correct information about the authorship of the non-human primate data used in this study.
