An Empirical Study of Modular Bias Mitigators and Ensembles
Talk, Workshop on Benchmarking Data for Data-Centric AI (DataPerf@ICML), Baltimore, MD
Talks and presentations
A Mixture of Personalized Experts for Human Affect Estimation
Talk, International Conference in Machine Learning and Data Mining (MLDM), Newark, New Jersey
We investigate the personalization of deep convolutional neural networks for facial expression analysis from still images. While prior work has focused on population-based (``one-size-fits-all'') approaches, we formulate and construct personalized models via a mixture of experts and supervised domain adaptation approach, showing that it improves greatly upon non-personalized models. Our experiments demonstrate the ability of the model personalization to quickly and effectively adapt to limited amounts of target data. We also provide a novel training methodology and architecture for creating personalized machine learning models for more effective analysis of emotion state.