Unsupervised Multi-Task Learning For 3D Subtomogram Image Alignment, Clustering and Segmentation

Enormous success has been achieved with deep neural networks consisting of standard linear-convolutions followed by simple non-linear mapping functions. in this paper, we add easily-computed non-linear local and global statistics to deep architectures, augmenting the information available at each layer. This additional information is then used in an identical manner to current processing. The summary statistics, which can be as simple as calculating within-channel variance, introduces little run-time computational overhead and can be instantiated with few extra parameters. All standard training procedures can be used without modification for training these augmented networks. We show, through extensive testing with ResNet on ImageNet, performance improvements across a wide range of network sizes. Additionally, we provide a detailed study of where within the deep networks these statistics are most effective.