Before revision:  On a high-quality publicly available cryoEM dataset and a difficult unpublished dataset supplied by the Shapiro lab, we show that CNNs trained with this objective classify particles accurately with very few positive training examples and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After revision:  On a high-quality publicly available cryoEM dataset and a difficult unpublished dataset supplied by the Shapiro lab, we show that CNNs trained with this objective classify particles accurately with very few positive training examples and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 } over published particles on three public cryoEM datasets without any post-processing . Furthermore, we show that incorporating an autoencoder improves generalization when very few labeled data points are available.