AcTiVis: Visual Exploration of Industry-Scale Deep Neural Network Models
AcTiVis: Visual Exploration of Industry-Scale Deep Neural Network Models
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DOI：
10.1109/tvcg.2017.2744718
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发表时间：
2018-01-01
影响因子：
5.2
通讯作者：
Chau, Duen Horng (Polo)
中科院分区：
文献类型：
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作者：
Kahng, Minsuk;Andrews, Pierre Y.;Chau, Duen Horng (Polo)
虽然深度学习模型已经在许多预测任务中实现了最先进的精度，但理解这些模型仍然是一个挑战。尽管最近人们对开发可视化工具来帮助用户解释深度学习模型感兴趣，但行业中部署的模型的复杂性和种类繁多，以及它们使用的大规模数据集，带来了现有工作无法充分解决的独特设计挑战。通过与 Facebook 超过 15 名研究人员和工程师的参与式设计会议，我们开发、部署并迭代改进了 ACTIVIS，这是一个用于解释大规模深度学习模型和结果的交互式可视化系统。通过紧密集成多个协调视图（例如模型架构的计算图概述以及用于模式发现和比较的神经元激活视图），用户可以在实例级别和子集级别探索复杂的深度神经网络模型。 ACTIVIS 已部署在 Facebook 的机器学习平台上。我们提供 Facebook 研究人员和工程师的案例研究，以及 ACTIVIS 如何与不同模型配合使用的使用场景。
While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge. Despite the recent interest in developing visual tools to help users interpret deep learning models, the complexity and wide variety of models deployed in industry, and the large-scale datasets that they used, pose unique design challenges that are inadequately addressed by existing work. Through participatory design sessions with over 15 researchers and engineers at Facebook, we have developed, deployed, and iteratively improved ACTIVIS, an interactive visualization system for interpreting large-scale deep learning models and results. By tightly integrating multiple coordinated views, such as a computation graph overview of the model architecture, and a neuron activation view for pattern discovery and comparison, users can explore complex deep neural network models at both the instance- and subset-level. ACTIVIS has been deployed on Facebook's machine learning platform. We present case studies with Facebook researchers and engineers, and usage scenarios of how ACTIVIS may work with different models.