title: Towards Better Analysis of Deep Convolutional Neural Networks.
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.
authors: Mengchen
authors: M
authors: Liu
authors: Jiaxin
authors: J
authors: Shi
authors: Zhen
authors: Z
authors: Li
authors: Chongxuan
authors: C
authors: Li
authors: Jun
authors: J
authors: Zhu
authors: Shixia
authors: S
authors: Liu