Deep Convolutional Networks have been very successful in visual recognition tasks recently. Previous works visualize learned features at different layers o help people to understand how CNNs learn visual recognition tasks. However they do not help to accelerate the training process. We use Paraview to provides both qualitative and quantitative visualization that help understand the learning procedure, tune the learning parameters and direct merging and pruning of neural networks.
X. Chen et al., "TensorViz: Visualizing the Training of Convolutional Neural Network using Paraview," Proceedings of the 1st Workshop on Distributed Infrastructures for Deep Learning (2017, Las Vegas, NV), Association for Computing Machinery (ACM), Dec 2017.
1st Workshop on Distributed Infrastructures for Deep Learning, DIDL 2017, Part of Middleware '17 (2017: Dec. 11-15, Las Vegas, NV)
Article - Conference proceedings
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15 Dec 2017