Towards Better Analysis of Deep Convolutional Neural Networks (1604.07043v3)

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.

• The paper introduces CNNVis, a novel visualization tool that reinterprets deep CNNs as directed acyclic graphs to enhance interpretability.
• It employs hybrid visualization techniques including hierarchical rectangle packing, matrix reordering, and biclustering-based edge bundling to simplify complex activation patterns.
• It validates the approach through case studies that diagnose training issues and guide network refinement for improved performance.

Towards Better Analysis of Deep Convolutional Neural Networks

The paper "Towards Better Analysis of Deep Convolutional Neural Networks," authored by Mengchen Liu et al., presents a substantial contribution to the field of visual analytics in deep learning by developing CNNVis, a tool aimed at facilitating the understanding and analysis of deep convolutional neural networks (CNNs). CNNs have established themselves as powerful tools in various pattern recognition tasks, including image and video classification. However, their complex architecture, often perceived as a "black box," presents significant challenges in interpreting the roles and interactions of individual neurons and connections. This paper addresses these issues by introducing a novel visualization approach.

CNN as a Directed Acyclic Graph

The authors begin by conceptualizing a deep CNN as a directed acyclic graph (DAG), where nodes represent neurons and edges signify connections between them. This abstraction enables the authors to apply advanced visualization techniques to provide insights into the complex interrelationships within a CNN. Specifically, they leverage a hierarchical rectangle packing algorithm to visualize derived features of neuron clusters, a matrix reordering algorithm to uncover activation patterns, and a biclustering-based edge bundling method to minimize visual clutter from numerous connections.

Hybrid Visualization Approach

CNNVis employs a hybrid visualization approach, integrating rectangle packing to portray neuron features, matrix visualizations for neuron activations, and edge bundling for simplifying the connectivity visualization. The hierarchical rectangle packing algorithm effectively showcases the features derived from neuron clusters, providing a clear, visual representation of their roles. The matrix reordering algorithm further organizes these visualizations, clarifying activation patterns within the network, and the edge bundling method addresses the challenge of visual clutter, making it easier to comprehend neuron interactions.

Evaluation and Case Studies

The efficacy of CNNVis is demonstrated through comprehensive evaluations on various CNN architectures and three specific case studies. These case studies illustrate CNNVis' ability to:

1. Analyze the influence of CNN architecture on performance, particularly focusing on depth and width. The visualization tool enables experts to identify issues like overfitting and neuron redundancy, which can guide architectural adjustments.
2. Diagnose failed training processes by uncovering issues such as negative weights leading to zero activations, facilitating effective troubleshooting and refinement of the CNN structure.
3. Assist in refining CNNs, offering experts the capability to iteratively enhance CNN design to achieve better convergence and performance.

Implications and Future Direction

The practical implications of this work are significant, offering researchers and practitioners a robust tool to dissect the inner workings of CNNs beyond trial-and-error methods. The visualization strategies proposed can streamline the debugging and refinement processes, leading to more efficient model development.

The paper also speculates on future enhancements to CNNVis, such as integrating it with online training processes to provide real-time updates, and extending its application to other neural network architectures, like recurrent neural networks (RNNs), which pose unique visualization challenges due to their feedback loops.

In conclusion, this work contributes a substantial methodological advancement in the analysis of deep CNNs, offering both theoretical insights and practical tools for the deep learning community. CNNVis stands out as an instrumental development in paving the way for more transparent and interpretable neural network models.