ClaVis

Heyen, Frank; Munz, Tanja; Neumann, Michael; Ortega, Daniel; Vu, Ngoc Thang; Weiskopf, Daniel; Sedlmair, Michael

doi:10.1145/3399715.3399814

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…This matrix shows which classes are confused with each other and how heavy the confusion is. Later efforts focus on generalizing the confusion matrix to other scenarios [20,21,27,28,60]. For example, Boxer [20] compares performance of different classifiers on the selected subsets by combining a confusion matrix with several chart visualizations.…”

Section: Model Evaluation In Visualizationmentioning

confidence: 99%

A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision

Chen,

Guo,

Tian

et al. 2023

IEEE Trans. Visual. Comput. Graphics

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Existing model evaluation tools mainly focus on evaluating classification models, leaving a gap in evaluating more complex models, such as object detection. In this paper, we develop an open-source visual analysis tool, Uni-Evaluator, to support a unified model evaluation for classification, object detection, and instance segmentation in computer vision. The key idea behind our method is to formulate both discrete and continuous predictions in different tasks as unified probability distributions. Based on these distributions, we develop 1) a matrix-based visualization to provide an overview of model performance; 2) a table visualization to identify the problematic data subsets where the model performs poorly; 3) a grid visualization to display the samples of interest. These visualizations work together to facilitate the model evaluation from a global overview to individual samples. Two case studies demonstrate the effectiveness of Uni-Evaluator in evaluating model performance and making informed improvements.

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Section: Model Evaluation In Visualizationmentioning

confidence: 99%

A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision

Chen,

Guo,

Tian

et al. 2023

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

show abstract

“…For instance, some techniques have been specifically designed for an analysis of classification models. For example, ClaVis [19] is a visual analytics system used to compare multiple classifiers.…”

Section: Related Workmentioning

confidence: 99%

Visual analytics tool for the interpretation of hidden states in recurrent neural networks

García

Munz

Weiskopf

2021

Vis. Comput. Ind. Biomed. Art

Self Cite

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In this paper, we introduce a visual analytics approach aimed at helping machine learning experts analyze the hidden states of layers in recurrent neural networks. Our technique allows the user to interactively inspect how hidden states store and process information throughout the feeding of an input sequence into the network. The technique can help answer questions, such as which parts of the input data have a higher impact on the prediction and how the model correlates each hidden state configuration with a certain output. Our visual analytics approach comprises several components: First, our input visualization shows the input sequence and how it relates to the output (using color coding). In addition, hidden states are visualized through a nonlinear projection into a 2-D visualization space using t-distributed stochastic neighbor embedding to understand the shape of the space of the hidden states. Trajectories are also employed to show the details of the evolution of the hidden state configurations. Finally, a time-multi-class heatmap matrix visualizes the evolution of the expected predictions for multi-class classifiers, and a histogram indicates the distances between the hidden states within the original space. The different visualizations are shown simultaneously in multiple views and support brushing-and-linking to facilitate the analysis of the classifications and debugging for misclassified input sequences. To demonstrate the capability of our approach, we discuss two typical use cases for long short-term memory models applied to two widely used natural language processing datasets.

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Trinary tools for continuously valued binary classifiers

Gleicher

Chen

2022

Visual Informatics

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ClaVis

Cited by 4 publications

References 36 publications

A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision

A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision

Visual analytics tool for the interpretation of hidden states in recurrent neural networks

Trinary tools for continuously valued binary classifiers

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