&lt;italic&gt;PowerSet&lt;/italic&gt;: A Comprehensive Visualization of Set Intersections

Alsallakh, Bilal; Liu, Ren

doi:10.1109/tvcg.2016.2598496

Cited by 28 publications

(11 citation statements)

References 39 publications

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“…The intuitive treemap-based design is suitable for comparing several datapaths [53] and has been proven effective in the case studies. We can further improve its scalability by adopting a less intuitive but more scalable set of visualization techniques, such as PowerSet [54].…”

Section: Discussionmentioning

confidence: 99%

Analyzing the Noise Robustness of Deep Neural Networks

Cao

Liu

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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A B C D E (a) Input images (b) Datapath visualization at the network-and layer-levels (c) Neuron visualization Figure 1: Explanation why an adversarial panda image is not classified as a panda. The root cause is identified as the neurons in the feature map F A failing to detect the outline of the panda's ear (E) in the adversarial example, which further leads to the failure of detecting the panda's ear (B) in F C1 . Abstract-Adversarial examples, generated by adding small but intentionally imperceptible perturbations to normal examples, can mislead deep neural networks (DNNs) to make incorrect predictions. Although much work has been done on both adversarial attack and defense, a fine-grained understanding of adversarial examples is still lacking. To address this issue, we present a visual analysis method to explain why adversarial examples are misclassified. The key is to compare and analyze the datapaths of both the adversarial and normal examples. A datapath is a group of critical neurons along with their connections. We formulate the datapath extraction as a subset selection problem and solve it by constructing and training a neural network. A multi-level visualization consisting of a network-level visualization of data flows, a layer-level visualization of feature maps, and a neuron-level visualization of learned features, has been designed to help investigate how datapaths of adversarial and normal examples diverge and merge in the prediction process. A quantitative evaluation and a case study were conducted to demonstrate the promise of our method to explain the misclassification of adversarial examples.

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Section: Discussionmentioning

confidence: 99%

Analyzing the Noise Robustness of Deep Neural Networks

Cao

Liu

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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“…PowerSet [34] is based on Treemaps [35], and displays intersections as rectangles, the area being proportional to the intersection cardinality. Intersections are sorted by degree (i.e.…”

Section: # Taskmentioning

confidence: 99%

RainBio: Proportional Visualization of Large Sets in Biology

Lamy

Tsopra

2020

IEEE Trans. Visual. Comput. Graphics

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Set visualization is a well-known task in information visualization. In biology, it is used for comparing visually sets of genes or proteins, typically using Venn diagrams. However, limitations of the Venn diagram are well-known: they are limited to 6 sets and difficult to read above 4. Many other set visualization techniques have been proposed, but they have never been widely used in biology. In this paper, we introduce RainBio, a technique for visualizing sets in biology and aimed at providing a global overview showing the size of the main intersections, in a proportional way, and the similarities between sets. We adapt rainbow boxes, a technique for visualizing small datasets, to the visualization of larger sets, using element aggregation and intersection clustering. We present the application of RainBio to three datasets, with 5, 6 and 12 sets. We also describe a small user study comparing RainBio with Venn diagrams, involving 30 students in biology. Results showed that RainBio led to significantly fewer errors on 6-set dataset, and that the majority of students preferred RainBio. RainBio is proposed as a web-based tool for up to 15 sets.

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“…Such predictions help reduce downtime and lower maintenance costs. We propose a novel solution called PowerSet [4] that addresses the limitations of traditional automated methods.…”

Section: Intelligent Hmi Example: Visual Analytics For Predictive Maimentioning

confidence: 99%

“…Therefore, there is a need to involve expert users in the process of extracting and evaluating the reliability and significance of these rules. We address this issue through our intelligent HMI solution called PowerSet [4].…”

Section: Intelligent Hmi Example: Visual Analytics For Predictive Maimentioning

confidence: 99%

“…Unlike automated machine-learning techniques, Visual Analytics leverage both historical data and specialists' expertise in the process of making the system more intelligent and adaptive. More details can be found in [4]. Our future efforts will focus on investigating new methods to analyze error development patterns more closely, taking into account their temporal and spatial dependencies.…”

Section: -2 / L Ren Invited Papermentioning

confidence: 99%

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