Diagnosing Concept Drift with Visual Analytics

Yang, Weikai; Li, Zhen; Liu, Mengchen; Lu, Yafeng; Cao, Kelei; Maciejewski, Ross; Liu, Shixia

doi:10.1109/vast50239.2020.00007

Cited by 30 publications

(20 citation statements)

References 61 publications

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“…Users can label instances recommended by an active learning algorithm or select informative instances to label with the help of visualization, which are used to further improve the underlying model. Such an integration is also substantiated by other work [25], [26], [27], [28], [29], [30], [31].…”

Section: Visualization For Annotation Quality Improvementsupporting

confidence: 76%

Towards Better Caption Supervision for Object Detection

Chen¹,

Wu²,

Wang³

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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Section: Visualization For Annotation Quality Improvementsupporting

confidence: 76%

Towards Better Caption Supervision for Object Detection

Chen¹,

Wu²,

Wang³

et al. 2022

IEEE Trans. Visual. Comput. Graphics

Self Cite

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“…Based on the analysis focus, these works can roughly be categorized into three groups. The first group concentrates on the input data of ML models to better understand the data distribution [8,63] or to better select the high-dimensional data features [25,39]. The second group focuses on the intermediate data representations from ML models to interpret how the data has been transformed internally.…”

Section: Related Workmentioning

confidence: 99%

VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations

Rathore¹,

Dev²,

Phillips³

et al. 2021

Preprint

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Fig. 1. With VERB, users can explore the high-dimensional word representations interactively before, during, and after applying bias mitigation techniques. (A) Embedding View highlights a subset of word embeddings using dimensionality reduction and visualizes the step-by-step transformations of their embeddings across various debiasing techniques. (B) Control Panel enables users to configure each debiasing technique and provides controls to iterate through each step of the transformation. (C) Explanation Panel gives a step-by-step description of the transformation. In this example, in the Embedding View, word representations are reoriented with the concept direction (Gender) along the x-axis. They are then transformed by a debiasing technique called the Linear Projection that removes this concept. These transformed representations are visualized again in (D) after a new dimensionality reduction, where there is no longer a well-defined gender concept, and hence the stereotypical associations are mitigated.

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“…Schneider et al [20] presented a visualization design space for dataset shift and a tool for comparing multi-dimensional feature distributions. In terms of specific types of dataset shift, concept drift is a topic that has garnered some attention [25,27]. Concept drift occurs when the relationship between the response variable and the features (i.e., 𝑃(𝑌 | 𝑿)) changes between training and testing.…”

Section: R Wmentioning

confidence: 99%

“…In this paper, we design and evaluate a new visual analysis interface for human users to identify covariate shift in image data. Although there exist several visualization work for detecting some types of dataset shift [4,20,25,27], we advance beyond the existing work in two aspects. First, our interface is designed to facilitate contrastive analysis between two different distributions for local regions [1,5], which is a key to covariate shift detection task.…”

mentioning

confidence: 99%