Test Train fc5 relu5 conv4-1 relu41 conv3-1 relu31 conv2-1 relu21 conv1-1 relu11 data CNNVis, a visual analytics system that helps experts understand, diagnose, and refine deep CNNs. Abstract-Deep convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks such as image classification. However, the development of high-quality deep models typically relies on a substantial amount of trial-and-error, as there is still no clear understanding of when and why a deep model works. In this paper, we present a visual analytics approach for better understanding, diagnosing, and refining deep CNNs. We formulate a deep CNN as a directed acyclic graph. Based on this formulation, a hybrid visualization is developed to disclose the multiple facets of each neuron and the interactions between them. In particular, we introduce a hierarchical rectangle packing algorithm and a matrix reordering algorithm to show the derived features of a neuron cluster. We also propose a biclustering-based edge bundling method to reduce visual clutter caused by a large number of connections between neurons. We evaluated our method on a set of CNNs and the results are generally favorable.
CNNVis REF ) models a deep neural network as a directed acyclic graph and applies several techniques to scale to large networks.
Towards Better Analysis of Deep Convolutional Neural Networks
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
Computer Science Medicine