Summary
Endert et al.’s paper “The State of the Art in Integrating Machine Learning into Visual Analytics” surveys the recent state-of-the-art models that integrate machine learning to visual analytics and highlights the advances achieved at the intersection of machine learning and visual analytics. In the data-driven era, how to make sense of data and how to facilitate a wider understanding of data attract the interests of researchers in various domains. It is challenging to discover knowledge from data while delivering reliable and interpretable results. Previous studies suggest that machine learning and visual analytics have complementary strengths and weaknesses, there are many works that explore the possibility to combine those two to develop interactive data visualization to promote sensemaking and analytical reasoning. This paper presents a survey of the achievements that have been made by recent state-of-the-art models. Also, it provides a summary of opportunities and challenges to boost the synergy between machine learning and visual analytics as future research directions.
Reflection
Overall, this paper presents a thorough survey of the progress that has been made by highlighting and synthesizing select research advances. The recent advances of deep learning models bring more new challenges and opportunities in the intersection of machine learning and visual analytics. We need to be aware the design of a highly accurate and efficient deep learning model is an iterative and progressive process of training, evaluation, and refinement, which typically relies on a time-consuming trial-and-error procedure where the parameters and the model structures are adjusted based on user expertise. Visualization researchers are making initial attempts to visually illustrate intuitive model behaviors and debug the training processes of widely-used deep learning models such as CNNs and RNNs. However, little effort has been conducted in tightly integrating state-of-the-art deep learning models with interactive visualizations to maximize the value of both. There is full potential in integrating deep learning into visual analytics for a better understanding of current practices.
As we know, the training of deep learning models requires a lot of data. However, sometimes well-labeled data is very expensive to obtain. The injection of a small number of user inputs into the models can potentially solve these problems through a visual analytics system. In real-world applications, a method is impractical if each specific task requires its own separate large-scale collection of training examples. To close the gap between academic research outputs and real-world requirements, it is necessary to reduce the sizes of required training sets by leveraging prior knowledge obtained from previously trained models in similar categories, as well as domain experts. Few-shot learning and zero-shot learning are two of the unsolved problems in the current practice of training deep learning models, which provide a possibility to incorporate prior knowledge on objects into a “prior” probability density function. That is, those models trained using given data and their labels can usually solve only pre-defined problems for which they were originally trained.
Discussion
I think the following questions are worthy of further discussion.
- What other challenges or opportunities can you think about a framework to incorporate machine learning and visual analytics?
- How to best leverage the advantages of machine learning and visual analytics in a complementary way?
- Do you plan to utilize a framework to incorporate machine learning and visual analytics in your course project? If yes, how do you plan to approach it?
- Are there any applications that we access in daily life you can think of as good examples that integrate machine learning into visual analytics?