Convolutional neural networks gain more and more popularity in image classification tasks since they are often even able to outperform human classifiers. While much research has been targeted towards network architecture optimization, the optimization of the labeled training data has not been explicitly targeted yet. Since labeling of training data is time-consuming, it is often performed by less experienced domain experts or even outsourced to online services. Unfortunately, this results in labeling errors, which directly impact the classification performance of the trained network. To overcome this problem, we propose an interactive visual analysis system that helps to spot and correct errors in the training dataset. For this purpose, we have identified instance interpretation errors, class interpretation errors and similarity errors as frequently occurring errors, which shall be resolved to improve classification performance. After we detect these errors, users are guided towards them through a two-step visual analysis process, in which they can directly reassign labels to resolve the detected errors. Thus, with the proposed visual analysis system, the user has to inspect far fewer items to resolve labeling errors in the training dataset, and thus arrives at satisfying training results more quickly.
Since CNNs are often treated as black boxes, many strive to visualize their functionality and thus give insight into how and why they work. While review many approaches integrating visual analytics and machine learning @cite_25 , we solely present work targeted towards the improvement of classifiers in general. We differentiate between visualizations that give insight into different aspects of classification through deep learning, and explain how our approach delineates from the existing body of related work.
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Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under-explored. This state-of-the-art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions.