Before revision:  To address this  , we develop a novel objective function, GE-binomial, for learning model parameters in this context. This objective uses a newly-formulated generalized expectation criteria to learn effectively from unlabeled data when using minibatched stochastic gradient descent optimizers. On a high-quality publicly available cryoEM dataset and a difficult unpublished dataset supplied by the Shapiro lab, we show that CNNs trained with this objective classify particles accurately with very few positive training examples and outperform EMAN2's byRef method by a large margin even with fewer labeled training examples }  . Furthermore, we show that incorporating an autoencoder improves generalization when very few labeled data points are available.
After revision:  To address this , we develop Topaz, an efficient and accurate particle picking pipeline using neural networks trained with few labeled particles by newly leveraging the remaining unlabeled particles through the framework of positive-unlabeled (PU) learning. Remarkably, despite using minimal labeled particles, Topaz allows us to improve reconstruction resolution by up to 0.15 } . Furthermore, we show that incorporating an autoencoder improves generalization when very few labeled data points are available.