  Neural Network is a powerful Machine Learning tool that shows outstanding
performance in Computer Vision, Natural Language Processing, and Artificial
Intelligence. In particular, recently proposed ResNet architecture and its
modifications produce state-of-the-art results in image classification
problems. ResNet and most of the previously proposed architectures have a fixed
structure and apply the same transformation to all input images. In this work,
we develop a ResNet-based model that dynamically selects Computational Units
(CU) for each input object from a learned set of transformations. Dynamic
selection allows the network to learn a sequence of useful transformations and
apply only required units to predict the image label. We compare our model to
ResNet-38 architecture and achieve better results than the original ResNet on
CIFAR-10.1 test set. While examining the produced paths, we discovered that the
network learned different routes for images from different classes and similar
routes for similar images.
