1. CNNComparator: Comparative Analytics of Convolutional Neural Networks
- Author
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Zeng, Haipeng, Haleem, Hammad, Plantaz, Xavier, Cao, Nan, and Qu, Huamin
- Subjects
FOS: Computer and information sciences ,Computer Science - Learning ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,H.1.2 ,Machine Learning (cs.LG) - 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., Comment: 5 pages. This paper has been accepted by VADL 2017: Workshop on Visual Analytics for Deep Learning
- Published
- 2017
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