CNNComparator: Comparative Analytics of Convolutional Neural Networks

Abstract

Convolutional neural networks (CNNs) are widely used in many image recognition tasks due to their extraordinary performance. However, training a good CNN model can still be a challenging task. In a training process, a CNN model typically learns a large number of parameters over time, which usually results in different performance. Often, it is difficult to explore the relationships between the learned parameters and the model performance due to a large number of parameters and different random initializations. In this paper, we present a visual analytics approach to compare two different snapshots of a trained CNN model taken after different numbers of epochs, so as to provide some insight into the design or the training of a better CNN model. Our system compares snapshots by exploring the differences in operation parameters and the corresponding blob data at different levels. A case study has been conducted to demonstrate the effectiveness of our system.

5 pages. This paper has been accepted by VADL 2017: Workshop on Visual Analytics for Deep Learning

References in corpus (4)

• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• Understanding Neural Networks Through Deep Visualization
• Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks
• Direct-Manipulation Visualization of Deep Networks

Cited by in corpus (5)

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• A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes
• On the Evolution of Neuron Communities in a Deep Learning Architecture